Techno Nusa Mandiri: Journal of Computing and Information Technology <p>The TECHNO Nusa Mandiri: Journal of Computing and Information Technology is a journal published by LPPM Universitas Nusa Mandiri. The TECHNO Nusa Mandiri:&nbsp;Journal of Computing and Information Technology was originally intended to accommodate scientific papers made by Informatics Engineering lecturers. TECHNO Nusa Mandiri Journal has ISSN: <a title="Print Media" href=";1180425415&amp;1&amp;&amp;" target="_blank" rel="noopener"><strong>1978-2136</strong></a> (Print Media) and <a title="Online Media" href=";1452590549&amp;1&amp;&amp;" target="_blank" rel="noopener"><strong>2527-676X</strong></a> (Online Media). The TECHNO Nusa Mandiri:&nbsp;Journal of Computing and Information Technology have the accredited National Journal status is accredited by the Indonesian Ministry of Research and Higher Education at the Sinta S4 level, in accordance with Decree on Strengthening SK Research and Development Number 21 / E / KPT / 2018 which has been in effect since July 9, 2018, for 5 years. Source: <a title="Salinan Surat Keputusan Peringkat Akreditasi Elektronik Periode I 2018" href="" target="_blank" rel="noopener">Risbang</a>. This journal is&nbsp;Rank 4 Accreditation Certificate (S4), Accreditation is valid for 5 years. Starting from Vol. 13, No. 1 the Year 2016 to Vol. 17, No. 1 the Year 2020.&nbsp;<span class="tlid-translation translation"><span title="">Journal of TECHNO Nusa Mandiri, re-accreditation remains at Rank 4 (SINTA 4), starting Vol. 16 No. 2 of 2019 based on the Decree of the Minister of Research and Technology / National Research and Innovation Agency Number 85/M/ KPT/2020, April 1, 2020</span></span></p> en-US <p>The copyright of any article in the TECHNO Nusa Mandiri Journal is fully held by the author under the Creative Commons CC BY-NC license.</p> <ol> <li class="show">The copyright in each article belongs to the author.</li> <li class="show">Authors retain all their rights to published works, not limited to the rights set out on this page.</li> <li class="show">The author acknowledges that Techno Nusa Mandiri: Journal of Computing and Information Technology (TECHNO Nusa Mandiri) is the first to publish with a Creative Commons Attribution 4.0 International license (CC BY-NC).</li> <li class="show">Authors can enter articles separately, manage non-exclusive distribution, from manuscripts that have been published in this journal into another version (for example: sent to author affiliation respository, publication into books, etc.), by acknowledging that the manuscript was published for the first time in Techno Nusa Mandiri: Journal of Computing and Information Technology (TECHNO Nusa Mandiri);</li> <li class="show">The author guarantees that the original article, written by the stated author, has never been published before, does not contain any statements that violate the law, does not violate the rights of others, is subject to the copyright which is exclusively held by the author.</li> <li class="show">If an article was prepared jointly by more than one author, each author submitting the manuscript warrants that he has been authorized by all co-authors to agree to copyright and license notices (agreements) on their behalf, and agrees to notify the co-authors of the terms of this policy. Techno Nusa Mandiri: Journal of Computing and Information Technology (TECHNO Nusa Mandiri) will not be held responsible for anything that may have occurred due to the author's internal disputes.</li> </ol> (Nurajijah) Thu, 29 Sep 2022 00:00:00 -0400 OJS 60 KLASIFIKASI STATUS STUNTING PADA BALITA MENGGUNAKAN METODE NAIVE BAYES DI KOTA MADIUN BERBASIS WEB <p>Stunting pada balita merupakan masalah gizi kronis yang sedang dialami dunia kesehatan. Anak dengan kondisi stunting mengalami kecenderungan penurunan tingkat kecerdasan, gangguan berbicara dan kesulitan dalam menangkap pembelajaran dalam metode yang biasa. Kota Madiun masih menghadapi tantangan dalam permasalahan gizi stunting. Prevalensi angka stunting tahun 2020 sebesar 10,18 persen atau 814 anak dari total 7.996 yang diukur. Penggunaan data mining dapat digunakan dalam berbagai bidang yang berhubungan dengan sekumpulan data yang banyak. Terdapat beberapa teknik pengerjaan data mining dalam pengambilan suatu informasi, diantaranya adalah klasifikasi. Umumnya klasifikasi status stunting menggunakan&nbsp;indeks TB/U atau tinggi badan dibanding usia. Pada penelitian ini, metode yang digunakan adalah metode naive bayes, yakni metode yang digunakan untuk memprediksi berbasis probabilitas, sistem yang dibangun menggunakan bahasa pemrograman python dan flask sebagai framework-nya. Dari hasil pengujian yang dilakukan menunjukkan bahwa metode naive bayes dapat digunakan dalam melakukan klasifikasi terhadap status stunting pada balita. Algoritma Naïve Bayes yang diimplementasikan ini, memiliki performansi nilai rata-rata yaitu akurasi sebesar 58%, precision sebesar 68%, dan recall sebesar 58% dari hasil pengujian confusion matrix dengan 30% data testing dan 70% data training.</p> Abdul Rozaq, Ari Joko Purnomo ##submission.copyrightStatement## Thu, 29 Sep 2022 22:05:30 -0400 Sentiment Analysis for Pharmaceutical Company from Social Media using Adaptive Compression (AdaComp) with Random Under Sample (RUS) and Synthetic Minority Over-sampling (SMOTE) <p>Pharmaceutical company has become the most highlight company across the world lately because of the pandemic. Despite of the high demand market in pharmaceutical company, about 94% of large company across the world having difficulty in their supply chain that indirectly affect their services. The purpose of this research is to compare word embedding with compression model by doing sentiment analysis about the entity to find the best model that give better accuracy rates&nbsp;based on the opinion of Twitter, Instagram and Youtube, as they are the largest &nbsp;platform that its many users to express their opinions about an individual or an instance. Data is retrieved from Twitter, Instagram and Youtube using the R-Studio application by utilizing their API library, then preprocessing and stored in a database. Next step is labeling&nbsp;the data and then train the data using word2Vec and LSTM, GloVe and LSTM and lastly using Adaptive Compression (adaComp) to compress the both model word embedding.&nbsp;Unfortunately, we got imbalanced dataset after labeling process, so we add sampling technique to sampling the dataset using Random Under Sample (RUS) and Synthetic Minority Over-sampling Technique (SMOTE).&nbsp;After the data are trained and tested, the results will be evaluated using Confusion Matrix to get the best Accuracy. With several models that have been carried out,applying adaComp is proven to increase accuracy. In the Word2Vec word embedding with LSTM model, applying adaComp increasing its accuracy from 77% to 81%.</p> Pamungkas Setyo Wibowo, Andry Chowanda ##submission.copyrightStatement## Thu, 29 Sep 2022 23:02:42 -0400