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INDONESIA
JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI
Published by STMIK Global Informatika MDP
ISSN : 24074322     EISSN : 25032933     DOI : -
Core Subject : Science,
Computer Science & IT
JATISI bekerja sama dengan IndoCEISS dalam pengelolaannya. IndoCEISS merupakan wadah bagi para ilmuwan, praktisi, pendidik, dan penggemar dalam bidang komputer, elektronika, dan instrumentasi yang menaruh minat untuk memajukan bidang tersebut di Indonesia. JATISI diterbitkan 2 kali dalam setahun (September dan Maret), makalah yang diterbitkan JATISI minimal terdiri dari 60% dari luar Sumatera Selatan, dan 40% dari Sumatera Selatan. Makalah yang diterbitkan melalui tahap review oleh reviewer yang berpengalaman dan sudah memiliki makalah yang diterbitkan di jurnal internasional yang terindeks SCOPUS.
Arjuna Subject : -
Articles 1,236 Documents
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Sistem Pakar Diagnosa Stunting Pada Balita Menggunakan Metode Naive Bayes Dea Sinta Nurhidayah
JATISI Vol 13 No 2 (2026): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v13i2.9032

Abstract

The problem of stunting among children in Indonesia has decreased in prevalence from 24.4% in 2021 to 21.6% in 2022. Therefore, the government really hopes that the prevalence of stunting will continue to decline in order to create healthy and stunting-free children. However, what is happening at Posyandu Wargo Rukun Dusun Dosaran, Klaten Regency is that there are still many parents' concerns about the prevalence of stunting in children, this has become a concern for parents. Due to the many factors of accessibility to health services and economic factors, many parents often diagnose themselves without adequate knowledge base and only wonder whether their child is stunted. To overcome this problem, this research aims to develop an "Expert System for Stunting Diagnosis in Toddlers Using the Naive Bayes Method" to help parents detect early symptoms of stunting in their children based on accurate and relevant information sources obtained from experts.
Rancang Bangun Website Kasir Dan Manajemen Stok Untuk Distributor Utama Bumbu 47 Di Surabaya Jocelyn Leora; Rinabi Tanamal
JATISI Vol 13 No 2 (2026): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v13i2.11879

Abstract

The advancement of information technology has significantly impacted the food distribution industry. The main distributor of Bumbu 47 in Surabaya faces challenges in recording transactions and stock management, which are still done manually, leading to recording errors and low operational efficiency. This study develops a web-based application to automate these processes. The application is developed using the Waterfall method with Laravel, MySQL, as well as HTML, CSS, and JavaScript technologies. Testing results show that the system can automate transaction recording, update stock in real-time, and generate sales reports. The implementation of this application has had a positive impact on improving recording accuracy and operational efficiency for the distributor.
Analisis dan Perancangan Ulang UI/UX SISKA UNSIKA Menggunakan Kuesioner Skala Likert Najwa Latifah
JATISI Vol 13 No 2 (2026): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v13i2.16951

Abstract

The Academic Information System (SISKA) of Singaperbangsa Karawang University (UNSIKA) is a platform used by students to manage various academic activities, such as course registration, viewing study results, class schedules, and other academic information. Although it has been widely used, there are still several issues that affect the quality of the user experience (UX) and user interface (UI). This study aims to evaluate the UI/UX of the SISKA UNSIKA website using a Likert scale questionnaire and provide redesign recommendations based on the results of user evaluation. The research method used is a descriptive quantitative approach with data collection through questionnaires distributed to 15 active UNSIKA students. The results show that the SISKA website obtained a satisfaction index score of 65.07%, which is categorized as good. The aspect with the highest score is the ease of understanding how to use the website, with 76.00%, while the aspects of color, icons, and layout received the lowest score at 57.33%. Based on the evaluation results, redesigns were made on several main pages, such as the dashboard, study plan, class schedule, thesis, and study results pages. The redesign resulted in a more modern, structured, and user-friendly appearance, and was able to improve user comfort and efficiency in accessing academic services.
Perbandingan Naive Bayes dan SVM untuk Analisis Sentimen Publik atas Vonis Korupsi Nadiem Makarim di YouTube Didik Iskandar; Yakiya Ratna Sari; Zesi Walikhsani; Risal Risal; Adin Fauzunan Toatilah
JATISI Vol 13 No 2 (2026): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v13i2.16999

Abstract

Media sosial telah berkembang menjadi salah satu sumber data yang relevan untuk mengidentifikasi persepsi publik terhadap berbagai isu, termasuk kasus korupsi yang memperoleh perhatian luas. Penelitian ini bertujuan untuk menganalisis sentimen masyarakat terhadap putusan kasus korupsi yang melibatkan Nadiem Anwar Makarim melalui komentar pada platform YouTube, sekaligus mengevaluasi kinerja algoritma Naive Bayes dan Support Vector Machine (SVM) dalam proses klasifikasi sentimen. Data penelitian diperoleh melalui teknik pengambilan komentar YouTube dan setelah melalui tahapan pembersihan data diperoleh sebanyak 3.025 komentar yang layak dianalisis. Proses penelitian diawali dengan tahapan text preprocessing, kemudian dilakukan pelabelan sentimen secara otomatis menggunakan leksikon InSet. Selanjutnya, representasi fitur dibentuk menggunakan metode Term Frequency-Inverse Document Frequency (TF-IDF),kemudian dilakukan klasifikasi menggunakan algoritma Naive Bayes dan SVM. Evaluasi performa model dilakukan berdasarkan metrik akurasi, precision, recall, F1-Score, serta confusion matrix. Selain itu, metode Latent Dirichlet Allocation (LDA) diterapkan untuk mengidentifikasi topik-topik dominan yang muncul pada data komentar sehingga mampu memberikan interpretasi yang lebih komprehensif terhadap hasil analisis sentimen. Hasil pelabelan menunjukkan bahwa sentimen negatif mendominasi data dengan proporsi sebesar 81,99%, sedangkan sentimen positif dan netral masing-masing sebesar 9,90% dan 8,11%. Berdasarkan hasil pengujian, algoritma SVM menghasilkan performa terbaik dengan nilai akurasi sebesar 83,72%, precision 0,8636, recall, 0,8272, dan F1-score 0,7554. Sementara itu, pemodelan topik menggunakan LDA menghasilkan nilai koherensi (C_v) sebesar 0,4233. Temuan penelitian ini mengindikasikan bahwa algoritma SVM memiliki kemampuan yang lebih baik dibandingkan Naive Bayes dalam mengklasifikasikan sentimen pada data komentar YouTube yang digunakan.
Prediksi Tingkat Stres Mahasiswa Berdasarkan Data Akademik dan Tugas Perkuliahan Menggunakan Algoritma Naïve Bayes Bella Intan
JATISI Vol 13 No 2 (2026): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v13i2.17030

Abstract

Academic stress is one of the most common challenges faced by university students due to academic demands, assignment workloads, and various psychological and environmental factors. This study aims to develop a model for predicting student stress levels using the Naïve Bayes algorithm based on psychological, health, and environmental data. The dataset consists of 1,100 records with 21 attributes, including anxiety level, self-esteem, depression, sleep quality, study load, social support, and other factors related to student stress. The research methodology includes data collection, data preprocessing, descriptive statistical analysis, data splitting into training and testing sets, and classification model development using the Naïve Bayes algorithm.
Analisis Opini Publik Terhadap Program Makan Bergizi Gratis (MBG) Pada Media Sosial Youtube Menggunakan Algoritma Support Vector Machine Bagas Andianto; Herny Februariyanti
JATISI Vol 13 No 2 (2026): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v13i2.17210

Abstract

The Free Nutritious Meals Program (MBG) is a government policy initiative aimed at supporting nutritional fulfillment and human resource development. However, its implementation has generated diverse public responses, making a measurable analysis necessary to identify trends in public opinion. This study aims to classify the sentiment of Youtube users comments toward the MBG Program using the Support Vector Machine (SVM) algorithm. The study employed a quantitative descriptive-analytical approach using comments from a video published by the Ngomongin Uang Youtube channel entitled “Program Makan Bergizi Gratis (MBG): Solusi Ekonomi atau Beban Negara?”. A total of 1.842 comments were collected through the Youtube Data API v3, of which 1,821 comments were retained after the data-cleaning process. The analytical stages included case folding, data cleaning, tokenization, stopword removal, stemming, lexicon-based labeling, TF-IDF weighting, and classification using LinearSVC. The results showed that 1.471 comments were classified as negative (80,78%), 222 comments as positive (12.19%), and 128 comments as neutral (7,03%). The model correctly predicted 300 out of 365 test data, achieving an overall accuracy of 82.19%. These findings indicate the dominance of critical responses and confirm the usefulness of SVM for mapping public opinion through Youtube comments

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