Jurnal Pilar Nusa Mandiri
type: channel
description: Jurnal PILAR Nusa Mandiri merupakan bentukan dari program studi Sistem Informasi, dimana awalnya merupakan media untuk menampung tulisan-tulisan ilmiah dosen-dosen program studi Sistem Informasi STMIK Nusa Mandiri Jakarta. Seiring perkembangan zaman, jurnal ini sudah menjadi jurnal Nasional yang memiliki P-ISSN: 1978-1946 dan E-ISSN: 2527-6514. PILAR Nusa Mandiri sudah menjadi Jurnal Terakreditasi Peringkat 3, dan sedang mencoba menuju menjadi jurnal terakreditasi yang lebih tinggi. Jurnal PILAR Nusa Mandiri terbit 2 kali dalam 1 tahun, yaitu di bulan Maret dan September
publisher: LPPM Universitas Nusa Mandiri
language: id
publication name: Jurnal Pilar Nusa Mandiri
issn: 1978-1946
copyright: An author who publishes in the Pilar Nusa Mandiri: Journal of Computing and Information System agrees to the following terms: Author retains the copyright and grants the journal the right of first publication of the work simultaneously licensed under the Creative Commons Attribution-NonCommercial 4.0 License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal Author is able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book) with the acknowledgement of its initial publication in this journal. Author is permitted and encouraged to post his/her work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of the published work (See The Effect of Open Access). Read more about the Creative Commons Attribution-NonCommercial 4.0 Licence here: https://creativecommons.org/licenses/by-nc/4.0/.
items: PADANG FOOD IMAGE CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK (CNN)
items: ANALYSIS OF THE NEED FOR AN INFORMATION SYSTEM ON PRICES AND AVAILABILITY OF BASIC MATERIALS
items: COMPARATIVE ANALYSIS OF RANDOM FOREST AND SUPPORT VECTOR CLASSIFIER FOR PREDICTING STUDENTS’ ON-TIME GRADUATION
items: ADDRESSING DIGITAL STARTUP FAILURE THROUGH THE AGILE METHODOLOGY APPROACH: A SYSTEMATIC LITERATURE REVIEW
items: EVALUATING PREPROCESSING EFFECTS IN NAME RETRIEVAL USING CLASSICAL IR AND CNN-BASED MODELS
items: COMPARATIVE PERFORMANCE OF TRANSFORMER AND LSTM MODELS FOR INDONESIAN INFORMATION RETRIEVAL WITH INDOBERT
items: IDENTIFICATION OF FOOD DIVERSIFICATION ON JAVA ISLAND USING ARCGIS
items: APPLICATION OF ARTIFICIAL NEURAL NETWORK METHODS TO DETECT HEART ATTACKS
items: SENTIMENT ANALYSIS ON TRAINING IMPLEMENTATION’S FEEDBACK IN PT XYZ
items: MACHINE LEARNING FOR EMPLOYMENT POSITION MAPPING
items: ISOLATION FOREST PARAMETER TUNING FOR MOBILE APP ANOMALY DETECTION BASED ON PERMISSION REQUESTS
items: WASTEWISE: AI-POWERED WASTE EDUCATION FOR ELEMENTARY STUDENTS USING YOLOV8 AND ESP32-CAM
items: PERCEPTION AND BARRIERS TO MOOC ADOPTION: A CASE STUDY OF KARTU PRAKERJA RECIPIENTS
items: UTILIZING END USER DEVELOPMENT METHOD FOR DEVELOPING PENCAK SILAT ORGANIZATION INFORMATION SYSTEMS
items: KOPTIHUB: A WAREHOUSE APPLICATION PROTOTYPE FROM COOPERATIVE PERS PECTIVE
items: STUDENT ATTENDANCE BASED ON FACE RECOGNITION USING THE CONVOLUTIONAL NEURAL NETWORK METHOD
items: DEVELOPMENT OF A FUNDRAISING WEBSITE WITH PAYMENT GATEWAY TO SUPPORT DIGITAL ECONOMY AT LAZISMU
items: COMPARISON OF ARIMA, LSTM, AND GRU MODELS FOR FORECASTING SALES OF HIT AEROSOL PRODUCTS
items: FINE-TUNING RESNET50V2 WITH ADAMW AND ADAPTIVE TRANSFER LEARNING FOR SONGKET CLASSIFICATION IN LOMBOK
items: PROJECT MANAGEMENT OF STEEL PLATE WAREHOUSE INVENTORY INFORMATION SYSTEM
items: AGGLOMERATIVE HIERARCHICAL CLUSTERING FOR REGIONAL GROUPING IN CENTRAL JAVA BASED ON WELFARE INDICES
items: EVALUATING FIKOM THESIS ADVISORY QUALITY WITH MANAGEMENT BY OBJECTIVES AT UNIVERSITAS MUSLIM INDONESIA
items: DETERMINATION OF POTENTIAL BUSINESS LOCATIONS USING DATA MINING CLUSTERING
items: PASSWORD STRENGTH STUDY USING THE ZXCVBN ALGORITHM AND BRUTE-FORCE TIME ESTIMATION TO STRENGTHEN CYBERSECURITY
items: MEASURING INFORMATION TECHNOLOGY GOVERNANCE USING COBIT 2019 FRAMEWORK AT TOURISM INDUSTRY
items: ONLINE DELIVERY, DINE-IN, AND RESERVATION SYSTEM USING THROW-AWAY PROTOTYPING AT JONG JAVA RESTAURANT
items: GENERATION Z'S AND ANDROID OS: HOW USER EXPERIENCE, SECURITY, AND SYSTEM PERFORMANCE SHAPE SATISFACTION
items: PREDICTING SOLAR POWER GENERATION: A MACHINE LEARNING APPROACH FOR GRID STABILITY AND EFFICIENCY
items: PERFORMANCE COMPARISON OF RANDOM FOREST REGRESSION, SVR MODELS IN STOCK PRICE PREDICTION
items: MAPPING OF DOMESTIC AND FOREIGN TOURIST VISITS IN EAST JAVA USING THE DBSCAN METHOD

PADANG FOOD IMAGE CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK (CNN)
type: item
description: The recognition of Padang traditional foods presents a challenge because of their high visual similarity, which makes manual classification difficult. This study aims to develop an automatic image classification model for Padang foods using the Convolutional Neural Network (CNN) algorithm. The dataset consisted of 1350 images across nine classes of Padang dishes including omelet, chili egg, cow tendon curry, stuffed intestine curry, fish curry, dendeng batokok, rendang, ayam pop, and fried chicken. The CNN architecture was trained for twenty epochs and evaluated using accuracy, loss, confusion matrix, and testing with new images. The results show that the model reached a final training accuracy of 70.2 percent and a validation accuracy of 65 percent, while testing with unseen images produced correct predictions with moderate confidence levels. These findings suggest that CNN is effective for classifying Padang traditional foods and can be applied in culinary promotion, digital food catalogs, and technology based ordering platforms.
subject: Articles
  topic: section
subject: convolutional neural network
  topic: keywords
subject: deep learning
  topic: keywords
subject: image classification
  topic: keywords
subject: image recognition
  topic: keywords
subject: Padang food
  topic: keywords
creator: Nabilah Putri Permana
creator: Syafri Arlis
rights: Hak Cipta (c) 2025 Nabilah Putri Permana, Syafri Arlis http://creativecommons.org/licenses/by-nc/4.0
date: 2025-09-29
publication date: 2025-09-29
starting page: 282
ending page: 289
doi: 10.33480/pilar.v21i2.7388

ANALYSIS OF THE NEED FOR AN INFORMATION SYSTEM ON PRICES AND AVAILABILITY OF BASIC MATERIALS
type: item
description: The development of information technology has driven digital transformation in various sectors, including the economic sector. Managing data on the prices and availability of basic commodities is crucial for maintaining community economic resilience. This study applies a design thinking approach to analyze the need for an information system on the prices and availability of basic commodities in Yogyakarta City, with a testing plan prepared using black box, white box, and security methods. The analysis produced three main findings: the need for Single Sign-On (SSO) with role-based access, real-time monitoring of commodity prices, and cross-agency integration in agenda and program management. The proposed system design consists of four main modules: administration, agenda, services, and programs/activities. Since this study is limited to the needs analysis and prototype design stage, empirical test results are not yet available. Nevertheless, the study provides an initial framework and foundation for cross-agency integration in the Yogyakarta City Government to support transparency, coordination, and control of basic commodity prices.
subject: Articles
  topic: section
subject: design thingking
  topic: keywords
subject: information system
  topic: keywords
subject: requirements analysis
  topic: keywords
subject: staple goods
  topic: keywords
creator: Andriyan Dwi Putra
creator: Diana Rohmaniah
rights: Hak Cipta (c) 2025 Andriyan Dwi Putra, Diana Rohmaniah http://creativecommons.org/licenses/by-nc/4.0
date: 2025-09-29
publication date: 2025-09-29
starting page: 273
ending page: 281
doi: 10.33480/pilar.v21i2.7240

COMPARATIVE ANALYSIS OF RANDOM FOREST AND SUPPORT VECTOR CLASSIFIER FOR PREDICTING STUDENTS’ ON-TIME GRADUATION
type: item
description: On-time graduation is one of the key indicators of educational quality in higher education. The influencing factors range from students’ internal issues and academic abilities to institutional policies. However, academic management has not yet been able to classify the data and analyze the underlying factors contributing to delayed graduation. By identifying these factors, management can formulate appropriate academic solutions or policies. The purpose of this study is to build a prediction model for on-time graduation using machine learning algorithms. This study compares the classification performance of the Random Forest algorithm and the Support Vector Classifier (SVC). The dataset, consisting of 1,298 student records, includes academic data such as study program, GPA, TOEFL score, cohort year, and study duration. Model performance was evaluated using accuracy, F1 score, and ROC-AUC metrics, followed by a confusion matrix analysis. The final evaluation revealed that the Random Forest algorithm achieved the best performance, with an accuracy of 91.86%, an F1 score of 91.86%, and a ROC-AUC of 97.39%. Meanwhile, the SVC model obtained an accuracy of 81.12% and an F1 score of 81.09%. Based on these results, it can be concluded that the Random Forest algorithm is more reliable as a prediction model in the academic domain. The main contribution of this study is the development of an early detection system for students at risk of delayed graduation. Furthermore, the findings can serve as a basis for designing more solution-oriented academic policies in accordance with the conditions at STIMIK Tunas Bangsa Banjarnegara.
subject: Articles
  topic: section
subject: machine learning algorithms
  topic: keywords
subject: on-time graduation
  topic: keywords
subject: student data analysis
  topic: keywords
subject: study duration
  topic: keywords
creator: Nurus Sarifatul Ngaeni
rights: Hak Cipta (c) 2025 Nurus Sarifatul Ngaeni http://creativecommons.org/licenses/by-nc/4.0
date: 2025-09-23
publication date: 2025-09-23
starting page: 247
ending page: 255
doi: 10.33480/pilar.v21i2.7048

ADDRESSING DIGITAL STARTUP FAILURE THROUGH THE AGILE METHODOLOGY APPROACH: A SYSTEMATIC LITERATURE REVIEW
type: item
description: Startups are recognized as emerging enterprises that contribute to job creation, economic stabilization, and national development. Digital startups are formed to address challenges within their environments. This study aims to provide solutions and preventive measures for digital startup failures, given the persistently high global failure rate of 90%. A systematic literature review (SLR) was conducted to identify Agile-based Critical Success Factors (CSFs), which were then mapped as solutions to mitigate digital startup failures. Based on the findings, the most significant contributing factor to the failure of digital startups is insufficient funding (i.e., running out of capital or financial resources). To address this issue, the agile method offers relevant solutions that can be mapped to the problem, namely the adoption of “Iterative Budget Management,” “Accurate Effort Estimation,” and “Risk Management Strategies.” This study provides practitioners with valuable insights, knowledge, and reference points regarding the critical success factors (CSFs) derived from agile practices, which can serve as strategic mechanisms for mitigating failure in early-stage startups. Moreover, the research is expected to contribute new theoretical understanding that informs potential solutions to prevent digital startup failure.
subject: Articles
  topic: section
subject: agile-based CSFs
  topic: keywords
subject: digital startups
  topic: keywords
subject: SLR methodology
  topic: keywords
subject: startup failure
  topic: keywords
creator: Kenedi Binowo
rights: Hak Cipta (c) 2025 Kenedi Binowo http://creativecommons.org/licenses/by-nc/4.0
date: 2025-09-23
publication date: 2025-09-23
starting page: 256
ending page: 265
doi: 10.33480/pilar.v21i2.7000

EVALUATING PREPROCESSING EFFECTS IN NAME RETRIEVAL USING CLASSICAL IR AND CNN-BASED MODELS
type: item
description: Information Retrieval (IR) systems are pivotal for efficient data management, particularly in tasks involving name searches and entity identification. This study evaluates text preprocessing techniques, including case folding, phonetic normalization, and gender tagging, that affect the performance of classical (TF-IDF, LSI) and CNN-based retrieval models for multilingual name matching. Using a dataset of 365,468 globally diverse names, this study implements a preprocessing pipeline featuring: Double Metaphone phonetic preprocessing (92% validation accuracy), gender disambiguation for unisex names (92% accuracy), and optimized n-gram tokenization for short names. Evaluation metrics include precision, recall, F1-score, and our novel Name Similarity Score (NSS), combining orthographic and phonetic preprocessing. Results show our full pipeline improves recall to 1.00 and F1-score by 37% while reducing false negatives by 63%. Key findings reveal: TF-IDF achieves superior recall (0.98 vs CNN’s 0.85), LSI handles cultural variants effectively, and CNNs deliver the highest precision (0.91 vs TF-IDF’s 0.70), particularly for unisex names. This work contributes both a scalable multilingual preprocessing framework and the NSS evaluation metric for robust name retrieval systems.
subject: Articles
  topic: section
subject: CNN
  topic: keywords
subject: information retrieval
  topic: keywords
subject: multilingual names
  topic: keywords
subject: name retrieval
  topic: keywords
subject: phonetic normalization
  topic: keywords
creator: Frizca Fellicita Marcelly
creator: Irwansyah Saputra
creator: Muhammad Bagus Andra
rights: Hak Cipta (c) 2025 Frizca Fellicita Marcelly, Irwansyah Saputra, Muhammad Bagus Andra http://creativecommons.org/licenses/by-nc/4.0
date: 2025-09-23
publication date: 2025-09-23
starting page: 218
ending page: 227
doi: 10.33480/pilar.v21i2.6884

COMPARATIVE PERFORMANCE OF TRANSFORMER AND LSTM MODELS FOR INDONESIAN INFORMATION RETRIEVAL WITH INDOBERT
type: item
description: Neural network-based Information Retrieval (IR), particularly with Transformer models, has gained prominence in information search technology. However, the application of this technology in Indonesian, a low-resource language, remains limited. This study aims to compare the performance of the LSTM model and IndoBERT for IR tasks in Indonesian. The dataset consists of 5,000 query–document pairs collected via scraping from three Indonesian news portals: CNN Indonesia, Kompas, and Detik. Evaluation was performed using MAP, MRR, Precision@5, and Recall@5 metrics. The results show that IndoBERT outperforms LSTM in all metrics with a MAP of 0.82 and MRR of 0.84, while LSTM only reached a MAP of 0.63 and MRR of 0.65. These findings confirm that Transformer models like IndoBERT are more effective at capturing semantic relevance between queries and documents, even with limited datasets.
subject: Articles
  topic: section
subject: information retrieval
  topic: keywords
subject: IndoBERT
  topic: keywords
subject: LSTM
  topic: keywords
subject: neural network
  topic: keywords
subject: retrieval
  topic: keywords
creator: Nendi Sunendar Sunendar
creator: Irwansyah Saputra
rights: Hak Cipta (c) 2025 Nendi Sunendar Sunendar, Irwansyah Saputra http://creativecommons.org/licenses/by-nc/4.0
date: 2025-09-23
publication date: 2025-09-23
starting page: 228
ending page: 233
doi: 10.33480/pilar.v21i2.6920

IDENTIFICATION OF FOOD DIVERSIFICATION ON JAVA ISLAND USING ARCGIS
type: item
description: Indonesia is addressing the challenges of food security and consumer preference also known as Food diversification. The research aims to analyze the potential of various local food sources as alternatives to rice, which is the dominant staple food in Indonesia, with a particular focus on geographic implications. Although local carbohydrate sources like corn, potatoes, and tubers are available, their adoption is limited and understudied in relation to geographic distribution and consumer behavior. This study integrates survey data and GIS-based spatial analysis to evaluate local food diversification potential. Findings show that while 100% of respondents consume rice, 48.7% have tried alternatives, with limited availability (41.03%) and higher costs (17.95%) as key barriers. With 94.7% expressing willingness to adopt new staples, the results suggest GIS-based decision support systems can guide effective, region-specific food policy interventions.
subject: Articles
  topic: section
subject: food diversification
  topic: keywords
subject: geographic information system
  topic: keywords
subject: rice
  topic: keywords
creator: Amir Murtako
creator: Faiqa Hadya Hanifa
creator: Eidelwise Gloria Effatha
creator: Sri Rezeki Candra Nursari
creator: Febri Maspiyanti
rights: Hak Cipta (c) 2025 Amir Murtako, Faiqa Hadya Hanifa, Eidelwise Gloria Effatha, Sri Rezeki Candra Nursari, Febri Maspiyanti http://creativecommons.org/licenses/by-nc/4.0
date: 2025-09-23
publication date: 2025-09-23
starting page: 208
ending page: 217
doi: 10.33480/pilar.v21i2.6570

APPLICATION OF ARTIFICIAL NEURAL NETWORK METHODS TO DETECT HEART ATTACKS
type: item
description: A heart attack is a medical emergency caused by restricted blood flow to the heart, commonly leading to myocardial infarction due to blood clots or fat accumulation. Early detection of heart disease is crucial to support prevention efforts and assist healthcare professionals in timely diagnosis and treatment. This study applies the Backpropagation Neural Network (BPNN) algorithm as an intelligent computing method for heart attack detection. Experimental results demonstrate a prediction accuracy of 96.47%, confirming the effectiveness of artificial neural networks in identifying heart attacks in patients. These findings highlight the potential of BPNN as a reliable and precise early detection system, which can support more accurate clinical decision-making and improve the effectiveness of heart attack prevention and treatment.
subject: Articles
  topic: section
subject: accurate diagnosis
  topic: keywords
subject: Artificial Neural Network Approach
  topic: keywords
subject: BPNN
  topic: keywords
subject: early detection
  topic: keywords
subject: heart attack
  topic: keywords
creator: Nasir Hamzah
creator: Yan Rianto
rights: Hak Cipta (c) 2025 Nasir Hamzah, Yan Rianto http://creativecommons.org/licenses/by-nc/4.0
date: 2025-09-23
publication date: 2025-09-23
starting page: 198
ending page: 207
doi: 10.33480/pilar.v21i2.6413

SENTIMENT ANALYSIS ON TRAINING IMPLEMENTATION’S FEEDBACK IN PT XYZ
type: item
description: Customer satisfaction is an important aspect in building a company's image, both for employees and external parties. In order to improve employee satisfaction and performance, training that organized by the company needs to receive feedback so that the training organizers can continue to provide the best service to employees who participate in the training. The large volume of feedback that must be processed in text form, leads to prolonged identification of comments and the omission of certain training programs from further analysis. This study applies text mining using sentiment analysis and Word Cloud visualization to evaluate the effectiveness of training methods and identify areas for improvement based on employee feedback on training programs at PT XYZ. The amount of data used after preprocessing was 48,910 open feedback responses from 4,314 training sessions consisting of three forms: classroom training, digital learning, and hybrid learning. The evaluation for clustering used the K-Means method, which turned out to use two optimal clusters based on the silhouette. Overall satisfaction with the training was determined through key points such as stable internet connection, overlapping of training schedule, and poor learning environment. Issues frequently that identified in the Word Cloud analysis revealed keywords describing positive and negative aspects of the situation that are requiring further improvement. This identification is useful for developing recommendations to enhance the implementation of the training and participants' experience. Further research may also involve advanced sentiment analysis and more accurate classification methods.
subject: Articles
  topic: section
subject: feedback
  topic: keywords
subject: k-means
  topic: keywords
subject: sentiment analysis
  topic: keywords
subject: training
  topic: keywords
subject: word cloud
  topic: keywords
creator: Fadilia Rinarwastu
creator: Imam Yuadi
rights: Hak Cipta (c) 2025 Fadilia Rinarwastu, Imam Yuadi http://creativecommons.org/licenses/by-nc/4.0
date: 2025-09-23
publication date: 2025-09-23
starting page: 178
ending page: 186
doi: 10.33480/pilar.v21i2.6641

MACHINE LEARNING FOR EMPLOYMENT POSITION MAPPING
type: item
description: Employee performance directly impacts organizational efficiency, yet traditional HR analytics often lack predictive precision. This study bridges HR theory and machine learning by evaluating tree-based algorithms for employee data analysis. Using a dataset of 15,227 employee records, we tested the Bagged Decision Tree algorithm, focusing on variables such as talent, career values, and aspirations. The Bagged Decision Tree achieved 98.65% accuracy, with talent and career values as key predictors. Excluding aspiration values reduced accuracy slightly to 98.57%, while excluding career values lowered it significantly to 92.13%. These findings highlight the robustness of the Bagged Decision Tree in HR analytics and emphasize the importance of variable selection, particularly career values and talent, in predicting performance outcomes. Future work should further explore real-world implementation challenges.
subject: Articles
  topic: section
subject: bagged decision tree
  topic: keywords
subject: employees
  topic: keywords
subject: machine learning
  topic: keywords
subject: organizational performance
  topic: keywords
subject: predictive analytics
  topic: keywords
creator: Sena Aditia Apriadi
creator: Hilman Ferdinandus Pardede
rights: Hak Cipta (c) 2025 Sena Aditia Apriadi, Hilman Ferdinandus Pardede http://creativecommons.org/licenses/by-nc/4.0
date: 2025-09-23
publication date: 2025-09-23
starting page: 266
ending page: 272
doi: 10.33480/pilar.v21i2.3028

ISOLATION FOREST PARAMETER TUNING FOR MOBILE APP ANOMALY DETECTION BASED ON PERMISSION REQUESTS
type: item
description: Ensuring mobile app security needs the capability to detect apps that request excessive or inappropriate permissions. This research proposes an anomaly detection approach using Isolation Forest, enhanced through hyperparameter tuning, to identify suspect apps based on permission request patterns. The dataset is processed into binary features, followed by exploratory data analysis (EDA) to examine the distribution and highlight sensitive permissions. The Isolation Forest model is then optimized by tuning parameters such as contamination level, number of estimators, and sample size. The fine-tuned model achieved a more accurate separation between normal and anomaly applications, detecting 10 anomalies out of 200 applications, with anomaly applications averaging 125.10 permits compared to 42.76 in normal applications. These anomalies often requested permissions related to network, storage, contacts and microphone, indicating potential privacy risks. The results show that parameter tuning improves the detection performance of Isolation Forest, providing a practical solution for mobile security monitoring. After tuning, the number of false positives decreased by 50%, and the model successfully reduced detected anomalies from 20 to 10, increasing the precision of anomaly detection from 70% to 90%. Future work could include improving feature selection and integration into real-time detection systems.
subject: Articles
  topic: section
subject: anomaly detection
  topic: keywords
subject: isolation forest
  topic: keywords
subject: parameter tuning
  topic: keywords
subject: permission requests
  topic: keywords
creator: Valencia Claudia Jennifer Kaunang
creator: Nur Alamsyah
creator: Reni Nursyanti
creator: Budiman Budiman
creator: Venia R Danestiara
creator: Elia Setiana
rights: Hak Cipta (c) 2025 Valencia Claudia Jennifer Kaunang, Nur Alamsyah, Reni Nursyanti, Budiman Budiman, Venia R Danestiara, Elia Setiana http://creativecommons.org/licenses/by-nc/4.0
date: 2025-09-23
publication date: 2025-09-23
starting page: 187
ending page: 197
doi: 10.33480/pilar.v21i2.6647

WASTEWISE: AI-POWERED WASTE EDUCATION FOR ELEMENTARY STUDENTS USING YOLOV8 AND ESP32-CAM
type: item
description: The growing volume of global waste poses a significant challenge for effective waste management, particularly in developing countries where awareness and practices around waste sorting remain limited. This study aims to enhance elementary school students' understanding and efficiency in sorting organic and inorganic waste using an interactive, AI-powered educational tool. The proposed system, WasteWise, integrates YOLOv8 for real-time object detection and ESP32-CAM for capturing waste images. A pre-test and post-test experimental design was conducted to assess students’ performance before and after using the system. The results showed a notable improvement in sorting accuracy, increasing from 60% with manual sorting to 90% using the WasteWise system, alongside reduced sorting time. These findings highlight the system's potential not only as an automated waste classification tool but also as a cost-effective and engaging platform for promoting environmental awareness and digital literacy among young learners.
subject: Articles
  topic: section
subject: wastewise
  topic: keywords
subject: YOLOv8
  topic: keywords
subject: ESP32-CAM
  topic: keywords
subject: artificial intelligence
  topic: keywords
creator: Kenny Aldi
creator: Yan Rianto
rights: Hak Cipta (c) 2025 Kenny Aldi, Yan Rianto http://creativecommons.org/licenses/by-nc/4.0
date: 2025-09-23
publication date: 2025-09-23
starting page: 171
ending page: 177
doi: 10.33480/pilar.v21i2.6414

PERCEPTION AND BARRIERS TO MOOC ADOPTION: A CASE STUDY OF KARTU PRAKERJA RECIPIENTS
type: item
description: The Indonesian government launched the Pre-Employment Card (Kartu Prakerja) program to enhance workforce skills and address economic challenges. This program provides training through online platforms, including Massive Open Online Courses (MOOCs). The UTAUT2 model was employed as a framework to understand the factors influencing the acceptance and use of educational technology in this context. This study examines the effects of UTAUT2 variables—performance expectancy, effort expectancy, habit, traditional barriers, platform content, access limitations, interaction limitations, facilitating conditions, hedonic value, price value, and social influence—on the intention and adoption of MOOCs among Pre-Employment Card participants. The sample consisted of 222 respondents who were users of the Prakerja platform. Data were collected using a questionnaire and analyzed through Structural Equation Modeling (SEM) with the support of PLS-SEM software. In addition, a sentiment analysis was conducted on comments posted on the official Instagram account @prakerja.go.id to explore public perceptions of the program. The findings reveal that 46.2 percent of public sentiment was negative, particularly related to the program implementation and the use of partner MOOC platforms. SEM analysis further indicates that hedonic value, habit, and social influence have positive and significant effects on the intention and adoption of MOOCs. The moderation analysis by gender shows that performance expectancy, hedonic value, and social influence are stronger among males, whereas effort expectancy, habit, and platform content are stronger among females.
subject: Articles
  topic: section
subject: MOOC
  topic: keywords
subject: PLS-SEM
  topic: keywords
subject: pre-employment card
  topic: keywords
subject: sentiment analysis
  topic: keywords
creator: Dendy Herdianto
creator: Nur Hendrasto
rights: Hak Cipta (c) 2025 Dendy Herdianto, Nur Hendrasto http://creativecommons.org/licenses/by-nc/4.0
date: 2025-09-23
publication date: 2025-09-23
starting page: 234
ending page: 246
doi: 10.33480/pilar.v21i2.6623

UTILIZING END USER DEVELOPMENT METHOD FOR DEVELOPING PENCAK SILAT ORGANIZATION INFORMATION SYSTEMS
type: item
description: Gondang is one of the PSHT sub-branches located in Sragen Regency, Central Java, Indonesia. In managing member data from recruitment to promotion, conventional methods are still used using office applications and information dissemination is still using brochures and social media. This research aims to develop an information system that can help manage data and disseminate information at PSHT Gondang. The system developed can manage the registration of prospective member to become a member and the process of promotion. Delivery of information in the form of organizational structures, announcements, activity schedules, services for member and community, activity galleries containing photos and videos can also be accessed through the system.EUD was chosen as a method in system development because time required is quite short with a relatively small cost allocation. The system is created using Laravel framework and Firebase as a database with a responsive display so that it can be accessed using a smartphone. By using the EUD method, users can modify the appearance and existing information if there is a change in data from the organization which was not available in previous research.
subject: Articles
  topic: section
subject: end user development
  topic: keywords
subject: PSHT
  topic: keywords
subject: system development
  topic: keywords
creator: Heribertus Ary Setyadi
creator: Hartati Dyah Wahyuningsih
creator: Galih Setiawan Nurohim
creator: Sundari Sundari
rights: Hak Cipta (c) 2025 Heribertus Ary Setyadi, Hartati Dyah Wahyuningsih, Doddy Satrya Perbawa, Sundari Sundari http://creativecommons.org/licenses/by-nc/4.0
date: 2025-09-23
publication date: 2025-09-23
starting page: 144
ending page: 152
doi: 10.33480/pilar.v21i2.6487

KOPTIHUB: A WAREHOUSE APPLICATION PROTOTYPE FROM COOPERATIVE PERS PECTIVE
type: item
description: Effective warehouse management is crucial for ensuring the availability of raw materials and smooth product distribution, particularly at Sentra Industri Kecil Somber (SIKS) Balikpapan, which specializes in soybean-based industries. Manual record-keeping has presented significant challenges, leading to recording errors, stock discrepancies, and delays in raw material procurement. To address these issues, a digital warehouse management prototype, "KoptiHub," was developed using a User-Centered Design (UCD) approach. This approach aimed to enhance inventory tracking efficiency, streamline raw material ordering, and improve overall product distribution. The prototype was evaluated using the System Usability Scale (SUS) with 15 cooperative administrators at SIKS Balikpapan. The evaluation yielded an SUS score of 82.17, resulting in an "A" grade, which indicates high usability and strong alignment with user expectations. Compared to previous warehouse management solutions, KoptiHub demonstrates superior usability, particularly in cooperative settings. However, further improvements, such as a simplified user interface and an AI-driven inventory forecasting feature, could enhance efficiency and accessibility. The results suggest that KoptiHub could serve as a scalable model for digitizing warehouse management in MSMEs and cooperatives, aligning with emerging trends in smart inventory management and supply chain optimization.
subject: Articles
  topic: section
subject: cooperative
  topic: keywords
subject: prototype
  topic: keywords
subject: system usability scale
  topic: keywords
subject: user centered design
  topic: keywords
subject: warehouse management
  topic: keywords
creator: Luh Made Wisnu Satyaninggrat
creator: Prasis Damai Nursyam Hamijaya
creator: Isnaini Nur Rachmawati
rights: Hak Cipta (c) 2025 Luh Made Wisnu Satyaninggrat, Prasis Damai Nursyam Hamijaya, Isnaini Nur Rachmawati http://creativecommons.org/licenses/by-nc/4.0
date: 2025-09-23
publication date: 2025-09-23
starting page: 160
ending page: 170
doi: 10.33480/pilar.v21i2.6456

STUDENT ATTENDANCE BASED ON FACE RECOGNITION USING THE CONVOLUTIONAL NEURAL NETWORK METHOD
type: item
description: Mataram University of Technology (UTM) still relies on a manual attendance process, such as signing paper-based attendance lists, which are prone to fraud and difficult to manage on a large scale. This study develops a face recognition-based attendance system using Convolutional Neural Network (CNN), which can automatically recognize visual patterns and unique facial features. CNN has advantages in extracting significant facial features, allowing it to recognize faces under various lighting conditions and viewing angles. The dataset used consists of 5,820 facial images from 97 students, with 60 augmented images per student. The results indicate that this system can be implemented in a lecture environment, achieving a validation accuracy of 98.5% at the 150th epoch. However, the model has some limitations, such as a relatively small dataset size and challenges in recognizing faces under extreme lighting conditions or unusual angles, which can affect accuracy in real-world applications. Additionally, although this system has the potential for real-time implementation, further optimization is required to ensure fast and accurate responses on a large scale. To overcome these limitations, future research can explore the use of direct camera input to enhance efficiency and user experience. Furthermore, improving dataset quality by incorporating variations in lighting and image angles, as well as exploring alternative deep learning architectures such as Vision Transformers (ViT) or Swin Transformer, can enhance model performance and generalization. By implementing these improvements, the facial recognition-based attendance system can be more optimal in enhancing accuracy and ease of use in academic environments.
subject: Articles
  topic: section
subject: attendance
  topic: keywords
subject: CNN
  topic: keywords
subject: face recognition
  topic: keywords
subject: students
  topic: keywords
creator: Salman Salman
creator: Hendri Ramdan
rights: Hak Cipta (c) 2025 Salman Salman, Hendri Ramdan http://creativecommons.org/licenses/by-nc/4.0
date: 2025-03-21
publication date: 2025-03-21
starting page: 117
ending page: 125
doi: 10.33480/pilar.v21i1.6157

DEVELOPMENT OF A FUNDRAISING WEBSITE WITH PAYMENT GATEWAY TO SUPPORT DIGITAL ECONOMY AT LAZISMU
type: item
description: This study discusses digital-based fundraising and financial services in the management of zakat, infaq, and shadaqah (ZIS). In this process, strategies and data related to distribution, calculation, management, and disbursement of funds often pose challenges. This issue is particularly evident in LAZISMU Bangka Belitung, which has yet to establish a website for large-scale online fundraising and for providing information about its available programs. Currently, the process of fundraising and ZIS management is still carried out conventionally. Therefore, this study proposes a digital system for LAZISMU that can provide information, manage ZIS, and facilitate digital payments through an integrated payment gateway. This innovation aims to make it easier for the public to fulfill their zakat obligations and access information about the fund distribution programs available at LAZISMU Bangka Belitung. The method used in developing this system is a prototype. The results of the study indicate that the developed system can simplify the process of fundraising and managing ZIS digitally. The implementation of this system is expected to enhance the efficiency of zakat management and have a broader positive impact in supporting communities in need.
subject: Articles
  topic: section
subject: computer science
  topic: keywords
subject: digital economy
  topic: keywords
subject: donation
  topic: keywords
subject: LAZISMU
  topic: keywords
subject: prototype
  topic: keywords
creator: Yudistira Bagus Pratama
creator: Hevitria Hevitria
creator: Rifki Hanif Setiawan
rights: Hak Cipta (c) 2025 Yudistira Bagus Pratama, Hevitria Hevitria, Rifki Hanif Setiawan http://creativecommons.org/licenses/by-nc/4.0
date: 2025-03-24
publication date: 2025-03-24
starting page: 134
ending page: 143
doi: 10.33480/pilar.v21i1.5716

COMPARISON OF ARIMA, LSTM, AND GRU MODELS FOR FORECASTING SALES OF HIT AEROSOL PRODUCTS
type: item
description: A more accurate forecasting model, such as LSTM, can significantly enhance business efficiency by providing more reliable predictions of future sales, allowing for better inventory management, optimized production schedules, and more precise distribution planning. This leads to reduced costs, minimized stockouts, and improved customer satisfaction. This study evaluates the forecasting performance of ARIMA, Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) models using sales data from 2021 to 2023. The models are assessed based on Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). Results show that LSTM outperforms the other models with a MAPE of 10.76%, followed by ARIMA at 11.23% and GRU at 11.47%. These findings highlight the advantages of deep learning methods, particularly LSTM, in capturing complex patterns and trends in time series data. The study demonstrates the potential of these models to optimize sales forecasting, aiding decision-making processes in production and distribution planning.
subject: Articles
  topic: section
subject: ARIMA
  topic: keywords
subject: GRU
  topic: keywords
subject: LSTM
  topic: keywords
subject: sales prediction
  topic: keywords
subject: time series forecasting
  topic: keywords
creator: Nendi Sunendar
creator: Yan Rianto
rights: Hak Cipta (c) 2025 Nendi Sunendar, Dr. Yan Rianto, M. Eng http://creativecommons.org/licenses/by-nc/4.0
date: 2025-09-23
publication date: 2025-09-23
starting page: 153
ending page: 159
doi: 10.33480/pilar.v21i2.6412

FINE-TUNING RESNET50V2 WITH ADAMW AND ADAPTIVE TRANSFER LEARNING FOR SONGKET CLASSIFICATION IN LOMBOK
type: item
description: This study aims to develop a classification system for traditional Lombok songket fabric patterns using the ResNet50V2 architecture, optimized through fine-tuning and the AdamW optimizer. The data were collected directly from songket artisans in Lombok and categorized into three groups based on the origin of the patterns: Sade, Sukarara, and Pringgasela. The model was trained with data augmentation techniques, including rotation, shifting, and zooming, to increase data diversity. During the training process, fine-tuning was applied to the last layer of ResNet50V2, and optimization was performed using AdamW with a learning rate of 0.0001. The model was evaluated using a confusion matrix, classification report, and analysis of accuracy and loss. The experimental results showed that the model achieved 100% accuracy at the 15th epoch. Furthermore, experiments with different parameters (epochs, batch size, and learning rate) demonstrated that the 15th epoch provided the best results with 100% accuracy, while using higher epochs (30 and 40) did not necessarily yield better outcomes. This model is effective in identifying songket fabric patterns with good classification results for each class. Although the results are excellent, increasing the dataset size and exploring more complex model architectures could further enhance performance. Overall, this study demonstrates the significant potential of deep learning technology in classifying songket patterns with reliable accuracy in real-world applications.
subject: Articles
  topic: section
subject: data augmentation
  topic: keywords
subject: deep learning
  topic: keywords
subject: fine-tuning
  topic: keywords
subject: ResNet50V2
  topic: keywords
subject: Songket pattern classification
  topic: keywords
creator: Erfan Wahyudi
creator: Bahtiar Imran
creator: Zaeniah
creator: Surni Erniwati
creator: Muh Nasirudin Karim
creator: Zumratul Muahidin
rights: Hak Cipta (c) 2025 Erfan Wahyudi, Bahtiar Imran, Zaeniah, Surni Erniwati, Muh Nasirudin Karim, Zumratul Muahidin http://creativecommons.org/licenses/by-nc/4.0
date: 2025-03-14
publication date: 2025-03-14
starting page: 82
ending page: 91
doi: 10.33480/pilar.v21i1.6485

PROJECT MANAGEMENT OF STEEL PLATE WAREHOUSE INVENTORY INFORMATION SYSTEM
type: item
description: Information system project management is an activity of available resources from an information system solution development project so that a system solution can be produced that meets predetermined objectives. From the findings in the field that the process of applying the inventory information system project management is still constrained because the previous business process lacks support and some still use ms. office excel in recording the process of entering and exiting goods becomes an obstacle if many transactions occur in a day, and inventory data tends to have differences from the warehouse and head office, so this study aims to apply the inventory information system project management that has been developed using the waterfall development method can function optimally and effectively by implementing the Project Management Body of Knowledge (PMBOK) method where the focus of discussion is work breakdown structure analysis, activity of arrow analysis, and project cost estimate analysis. The results of this study obtained the results of stage-based WBS analysis, activity of arrow analysis with 58 days, while project cost estimate analysis with 14% for the communication stage, 20% for the planning stage, 57% for the modeling stage, 4% for the construction stage, 5% deployment stage.
subject: Articles
  topic: section
subject: information technology planning
  topic: keywords
subject: PMBOK
  topic: keywords
subject: project management
  topic: keywords
creator: Samidi Samidi
creator: Fitrah Romadhan
rights: Hak Cipta (c) 2025 Samidi Samidi, Fitrah Romadhan http://creativecommons.org/licenses/by-nc/4.0
date: 2025-03-17
publication date: 2025-03-17
starting page: 100
ending page: 107
doi: 10.33480/pilar.v21i1.6258

AGGLOMERATIVE HIERARCHICAL CLUSTERING FOR REGIONAL GROUPING IN CENTRAL JAVA BASED ON WELFARE INDICES
type: item
description: Central Java Province comprises 35 regencies/cities with diverse welfare characteristics. These variations present challenges for the government in formulating targeted development policies. This study aims to group regions in Central Java based on welfare indices to support more effective policy planning. The Agglomerative Hierarchical Clustering method with the Average Linkage approach is applied to cluster the regions based on three attributes: Human Development Index, Uninhabitable Houses, and Economic Growth Rate. Data were obtained from the Central Java Provincial Social Service and the official website of the Central Statistics Agency (BPS) and processed using the proposed method. Experimental results indicate three clusters with proportions: 32 regions in cluster 1 (91.4%), 2 regions in cluster 2 (5.7%), and 1 region in cluster 3 (2.9%). Regions with higher welfare dominate the first cluster, while the second and third clusters include regions facing more significant welfare challenges. Clustering results were evaluated using the Silhouette Score (0.535) and Davies-Bouldin Index Score (0.610), demonstrating that the applied method effectively grouped regions based on the specified attributes. The findings of this study are anticipated to lay the groundwork for more directed and effective development policies.
subject: Articles
  topic: section
subject: agglomerative hierarchical clustering
  topic: keywords
subject: average linkage
  topic: keywords
subject: central java
  topic: keywords
subject: welfare indices
  topic: keywords
creator: Raafi Kurnia Desita
creator: Amiq Fahmi
creator: Asih Rohmani
creator: MY. Teguh Sulistyono
rights: Hak Cipta (c) 2025 Raafi Kurnia Desita, Amiq Fahmi, Asih Rohmani, MY. Teguh Sulistyono http://creativecommons.org/licenses/by-nc/4.0
date: 2025-03-19
publication date: 2025-03-19
starting page: 108
ending page: 116
doi: 10.33480/pilar.v21i1.6445

EVALUATING FIKOM THESIS ADVISORY QUALITY WITH MANAGEMENT BY OBJECTIVES AT UNIVERSITAS MUSLIM INDONESIA
type: item
description: The Faculty of Computer Science (FIKOM) at Universitas Muslim Indonesia (UMI) faces significant challenges in enhancing the quality of thesis supervision due to an increasing student population. This study utilizes the Management by Objectives (MBO) approach to evaluate and improve faculty supervision quality. MBO involves setting clear goals, monitoring progress, providing feedback, and evaluating performance based on Key Performance Indicators (KPIs), Customer Satisfaction Scores (CSAT), and Customer Effort Scores (CES). Data was gathered from questionnaires distributed to 211 FIKOM students currently writing or who have completed their theses. The findings reveal that MBO implementation significantly enhances communication between faculty and students, clarifies supervision goals, and boosts student satisfaction. The structured and directed approach of MBO makes the supervision process more efficient, leading to higher quality thesis completions. Additionally, the research underscores the importance of aligning supervision schedules and methods to better fit both faculty and student needs, thus mitigating issues related to faculty workload and student guidance. The study concludes that adopting MBO in thesis supervision processes can substantially improve both the effectiveness and satisfaction of academic guidance at FIKOM UMI.
subject: Articles
  topic: section
subject: key performance index
  topic: keywords
subject: management by objectives
  topic: keywords
subject: thesis supervision
  topic: keywords
subject: quality evaluation
  topic: keywords
creator: Najwan Firdaus Haris
creator: Lilis Nur Hayati
creator: Dewi Widyawati
rights: Hak Cipta (c) 2025 Najwan Firdaus Haris, Lilis Nur Hayati, Dewi Widyawati http://creativecommons.org/licenses/by-nc/4.0
date: 2025-03-17
publication date: 2025-03-17
starting page: 92
ending page: 99
doi: 10.33480/pilar.v21i1.5734

DETERMINATION OF POTENTIAL BUSINESS LOCATIONS USING DATA MINING CLUSTERING
type: item
description: Potential locations for businesses are highly sought after by business people to set up, expand their business, or establish a new business. Limited information on potential business locations is still a problem faced by many business people in making business decisions. The purpose of this research is to overcome the limitations of potential business location information. The approach used is the K-Means data mining clustering method which is compared to the Gaussian Mixture Model. The dataset used is residential, road access data and business points that already exist around the location. Both clustering methods are compared to the model evaluation method to determine the model with the best performance. The results show that the clustering method with the K-Means algorithm is the clustering model with the best performance. The results of the clustering resulted in 2 clusters, one of which is a cluster of potential business locations of 1041 locations. The conclusion of this study is that data mining clustering can be used to determine the optimal business location cluster. The results of this study can be recommended for business people to look for potential business locations, and for local governments to publicize potential business locations in order to attract investors from outside.
subject: Articles
  topic: section
subject: clustering
  topic: keywords
subject: data mining
  topic: keywords
subject: gaussian mixture model
  topic: keywords
subject: k-means
  topic: keywords
subject: optimal business location
  topic: keywords
creator: Dian Erdiansyah
creator: Indra Nugraha Abdullah
creator: Amandus Jong Tallo
rights: Hak Cipta (c) 2025 Dian Erdiansyah, Indra Nugraha Abdullah, Amandus Jong Tallo http://creativecommons.org/licenses/by-nc/4.0
date: 2025-03-14
publication date: 2025-03-14
starting page: 72
ending page: 81
doi: 10.33480/pilar.v21i1.6295

PASSWORD STRENGTH STUDY USING THE ZXCVBN ALGORITHM AND BRUTE-FORCE TIME ESTIMATION TO STRENGTHEN CYBERSECURITY
type: item
description: This research analyzes password strength based on its length and complexity using brute force attack simulations. The study begins with collecting password data from various sources to ensure sufficient variation in complexity levels. Next, the passwords are evaluated using the Zxcvbn algorithm, which provides a strength score as well as information about the time required to crack them. The same passwords are also evaluated using Brute-force Time Estimation to calculate the estimated time required to crack the password. After both algorithms have been evaluated, the results are analyzed to find the correlation between the Zxcvbn score and the estimated brute force time. The results of the data analysis are then visualized in the form of graphs or diagrams to facilitate understanding and assessment of password security. This simulation estimates the time required to guess a password, depending on the level of password complexity. Although the simulation results show that long and complex passwords are more secure, the actual strength of the password is highly dependent on the tools used by the attacker. In addition, digital security is not only limited to passwords, but also depends on various loopholes that can be exploited, such as personal data leaks or software vulnerabilities. Therefore, a comprehensive security approach is essential to protect users from potential cyberattacks. This study aims to provide in-depth insights into the strength and vulnerability of passwords and the effectiveness of algorithms in assessing password security.
subject: Articles
  topic: section
subject: brute-force estimation
  topic: keywords
subject: password
  topic: keywords
subject: zxcvbn
  topic: keywords
creator: Whisnu Yudha Saputra
creator: Sugiarti Sugiarti
creator: Haris Junianto
creator: Didit Suhartono
rights: Hak Cipta (c) 2025 Whisnu Yudha Saputra, Sugiarti Sugiarti, Haris Junianto, Didit Suhartono http://creativecommons.org/licenses/by-nc/4.0
date: 2025-03-14
publication date: 2025-03-14
starting page: 52
ending page: 59
doi: 10.33480/pilar.v21i1.6119

MEASURING INFORMATION TECHNOLOGY GOVERNANCE USING COBIT 2019 FRAMEWORK AT TOURISM INDUSTRY
type: item
description: Although PT XYZ has adopted information technology, it has not formally assessed its governance, leading to persistent issues in IT management, human resource capabilities, and alignment with business processes. This study evaluates IT governance at PT XYZ, a company in the travel and tourism industry, where rapid technological advancements have impacted operations. Using the COBIT 2019 framework, the study assessed IT governance through interviews and literature review, focusing on the domains APO04 – Managed Innovation, BAI02 – Managed Requirements Definition, BAI03 – Managed Solution Identification & Build, and BAI05 – Managed Organizational Change. The results indicate that these domains are at level 2, "Largely Achieved," highlighting areas of improvement. This benchmark provides practical recommendations to enhance IT governance and improve integration between IT and business functions. The findings offer PT XYZ actionable steps to strengthen governance practices, improve organizational performance, and better align technology with strategic business goals.
subject: Articles
  topic: section
subject: COBIT 2019
  topic: keywords
subject: corporate governance
  topic: keywords
subject: integration
  topic: keywords
subject: IT governance
  topic: keywords
creator: Nicholas Lee
creator: Wella Wella
rights: Hak Cipta (c) 2025 Nicholas Lee, Wella Wella http://creativecommons.org/licenses/by-nc/4.0
date: 2025-03-14
publication date: 2025-03-14
starting page: 44
ending page: 51
doi: 10.33480/pilar.v21i1.6060

ONLINE DELIVERY, DINE-IN, AND RESERVATION SYSTEM USING THROW-AWAY PROTOTYPING AT JONG JAVA RESTAURANT
type: item
description: The culinary industry continues to grow amidst the increasing purchasing power and consumption of society. Many restaurants still rely on direct sales (dine-in) without utilizing technology to enhance services and expand their market. This study aims to design and build a web-based information system for Restoran Jong Java, which includes dine-in, online delivery, and table reservation features. The system is expected to simplify transaction processes, improve operational efficiency, and provide added value for the restaurant in the industry competition. The system is developed using the throw-away prototyping method. This method allows for iterative system development with direct input from users. Testing is conducted using black-box testing to ensure the system functions as per the specifications, and user acceptance testing is performed through questionnaires with five main types of users: customers, admins, cashiers, waiters, and waitresses. The designed system is capable of supporting the integrated management of dine-in services, reservations, and online deliveries. The tests show that the system meets user needs, with a high level of satisfaction from the respondents. The user acceptance testing in this study shows positive results across different user groups. For the customer group, the average score obtained was 77.25%, the waiter group gave an average score of 83.6%, and for the managers, the system was also well received by them. This system has successfully improved the restaurant's operational efficiency and provided convenience for customers in making orders. It also serves as a technological solution that can help Jong Java expand its market reach and increase competitiveness.
subject: Articles
  topic: section
subject: black box testing
  topic: keywords
subject: dine-in
  topic: keywords
subject: online delivery
  topic: keywords
subject: reservation system
  topic: keywords
subject: throw-away prototyping
  topic: keywords
creator: Titasari Rahmawati
creator: Edwin Meinardi Trianto
creator: William Eka Tjipta
rights: Hak Cipta (c) 2025 Titasari Rahmawati, Edwin Meinardi Trianto, William Eka Tjipta http://creativecommons.org/licenses/by-nc/4.0
date: 2025-03-14
publication date: 2025-03-14
starting page: 24
ending page: 33
doi: 10.33480/pilar.v21i1.6139

GENERATION Z'S AND ANDROID OS: HOW USER EXPERIENCE, SECURITY, AND SYSTEM PERFORMANCE SHAPE SATISFACTION
type: item
description: This paper investigates Generation Z's opinion of the Android OS through an analysis of system performance, security, and user experience and their influence on general satisfaction. With Technology Adaptation (Z) as a mediator, the study focuses on three main factors: User Experience (X1), Security and Privacy (X2), and System Performance and Stability (X3), specifically among students who use Android devices. Data were collected through a structured questionnaire, and the analysis was conducted using the Partial Least Squares (PLS) method to evaluate the relationships between the variables. The findings reveal that, in addition to frequent updates, Generation Z's satisfaction is significantly influenced by the accessibility, performance, and security features of Android. The results highlight the importance of a positive user experience and robust security measures in enhancing user satisfaction. Continuous development in these areas is crucial for improving user engagement and contentment with Android devices.
subject: Articles
  topic: section
subject: android os
  topic: keywords
subject: generation z
  topic: keywords
subject: security and privacy
  topic: keywords
subject: user experience
  topic: keywords
subject: user satisfaction
  topic: keywords
creator: Cahyasari Kartika Murni
creator: Achmad Firman Choiri
creator: Fadhel Akhmad Hizham
rights: Hak Cipta (c) 2025 Cahyasari Kartika Murni, Achmad Firman Choiri, Fadhel Akhmad Hizham http://creativecommons.org/licenses/by-nc/4.0
date: 2025-03-14
publication date: 2025-03-14
starting page: 60
ending page: 71
doi: 10.33480/pilar.v21i1.6082

PREDICTING SOLAR POWER GENERATION: A MACHINE LEARNING APPROACH FOR GRID STABILITY AND EFFICIENCY
type: item
description: In countries with high levels of insolation, the demand for renewable energy sources has driven the rapid emergence and growth of solar power plants. Maintaining grid stability and efficient power management in response to weather variations that affect solar radiation intensity and battery consumption limits remains a major challenge. This study aims to develop a machine learning-based prediction model to estimate the electricity generated by solar power plants using weather data. Four algorithms are utilized: Linear Regression, Random Forest Regressor, Decision Tree Regressor, and Gradient Boosting Regressor. The results show that the Random Forest algorithm produces the best model, with MAE and RMSE values of 0.1114281 and 0.3187232, respectively. This research contributes to the literature, particularly on the relatively unexplored topic of using multiple machine learning models to predict energy output from photovoltaic systems. The findings have the potential to inform more efficient energy policies and improve energy integration technologies for grid-connected solar power systems.
subject: Articles
  topic: section
subject: energy forecasting
  topic: keywords
subject: machine learning
  topic: keywords
subject: renewable energy
  topic: keywords
creator: Popong Setiawati
creator: Adhitio Satyo Bayangkari Karno
creator: Widi Hastomo
creator: Ellya Sestri
creator: Dian Kasoni
creator: Dodi Arif
creator: Fahrul Razi
rights: Hak Cipta (c) 2025 Popong Setiawati, Adhitio Satyo Bayangkari Karno, Widi Hastomo, Ellya Sestri, Dian Kasoni, Dodi Arif, Fahrul Razi http://creativecommons.org/licenses/by-nc/4.0
date: 2025-03-14
publication date: 2025-03-14
starting page: 34
ending page: 43
doi: 10.33480/pilar.v21i1.6126

PERFORMANCE COMPARISON OF RANDOM FOREST REGRESSION, SVR MODELS IN STOCK PRICE PREDICTION
type: item
description: The stock market is characterized by high volatility and complexity, making it an intriguing and challenging subject for researchers and practitioners. This study aims to predict stock prices by comparing the performance of two machine learning models: Random Forest Regression and Support Vector Regression (SVR). These models were selected for their ability to handle complex data and high volatility. The dataset used in this study consists of BNI stock data over the last five years (2019–2024), comprising a total of 1,211 data points. Testing was conducted using a cross-validation approach, and model performance was evaluated based on several metrics, including MSE, R², RMSE, MAPE, MAE, and Score. The results indicate that Random Forest Regression outperforms SVR. The model achieved an MAE of 17.766, an RMSE of 22.376, and an R² of 0.997. These findings suggest that Random Forest Regression is more effective in predicting stock prices, particularly in unstable market conditions. This study recommends Random Forest Regression as a reliable model for stock price prediction, with potential applications in other stock markets with similar characteristics.
subject: Articles
  topic: section
subject: machine learning
  topic: keywords
subject: random forest regression
  topic: keywords
subject: stock price prediction
  topic: keywords
subject: support vector regression
  topic: keywords
creator: Maysas Yafi Urrochman
creator: Hasyim Asy'ari
creator: Fadhel Akhmad Hizham
rights: Hak Cipta (c) 2025 Maysas Yafi Urrochman, Hasyim Asy'ari, Fadhel Akhmad Hizham http://creativecommons.org/licenses/by-nc/4.0
date: 2025-03-14
publication date: 2025-03-14
starting page: 16
ending page: 23
doi: 10.33480/pilar.v21i1.6072

MAPPING OF DOMESTIC AND FOREIGN TOURIST VISITS IN EAST JAVA USING THE DBSCAN METHOD
type: item
description: Tourism is important in economic growth and regional development, especially in East Java Province with diverse tourist attractions. However, the mapping of domestic and foreign tourist visit patterns in this province is still limited. For this reason, this study uses the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method which can group density-based data without determining the number of clusters from the beginning and handle noise. The study aims to map districts/cities in East Java based on the number of tourist visits from 2018 to 2022, using visit data from the East Java Provincial Culture and Tourism Office. The analysis results show that in domestic tourist data, with parameters MinPts = 3 and ε = 1.00, one main cluster is formed consisting of 31 tourist locations and 7 noisy locations. In foreign tourist data, with ε = 0.6 and MinPts = 3, there is one cluster with 30 tourist locations and 8 other locations are categorized as noisy. Noisy locations tend to have higher visits but do not fit into the main cluster. These findings provide important insights for more targeted tourism promotion strategies and efficient resource allocation in East Java.
subject: Articles
  topic: section
subject: clustering
  topic: keywords
subject: DBSCAN
  topic: keywords
subject: domestic
  topic: keywords
subject: foreign
  topic: keywords
subject: tourism
  topic: keywords
creator: Marita Qori'atunnadyah
rights: Hak Cipta (c) 2025 Marita Qori'atunnadyah http://creativecommons.org/licenses/by-nc/4.0
date: 2025-03-14
publication date: 2025-03-14
starting page: 9
ending page: 15
doi: 10.33480/pilar.v21i1.6073