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All Journal Elkom: Jurnal Elektronika dan Komputer CESS (Journal of Computer Engineering, System and Science) NUR EL-ISLAM : Jurnal Pendidikan dan Sosial Keagamaan JSiI (Jurnal Sistem Informasi) JURIKOM (Jurnal Riset Komputer) JURTEKSI Jurnal Riset Informatika Jurnal Informatika dan Rekayasa Elektronik Jurnal Sistem Komputer dan Informatika (JSON) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) KLIK: Kajian Ilmiah Informatika dan Komputer Instal : Jurnal Komputer International Journal Software Engineering and Computer Science (IJSECS) el-Madib: Jurnal Pendidikan Dasar Islam Jurnal Komtekinfo Indonesian Health Issue Data Sciences Indonesia (DSI) VISA: Journal of Vision and Ideas Da'watuna: Journal of Communication and Islamic Broadcasting The Indonesian Journal of Computer Science SAMBARA: Jurnal Pengabdian Kepada Masyarakat INOVTEK Polbeng - Seri Informatika Sriwijaya Foodtech Journal MUTAADDIB: Islamic Education Journal
Sriani
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Religion Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Medicine & Pharmacology Nursing Physics Public Health Social Sciences Veterinary Other

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Analisis Sentimen Masyarakat Terhadap Resesi Ekonomi Global 2023 Menggunakan Algoritma Naïve Bayes Classifier Sriani; Lubis, Aidil Halim; Harahap, Yunus Fadillah
Elkom: Jurnal Elektronika dan Komputer Vol. 16 No. 2 (2023): Desember : Jurnal Elektronika dan Komputer
Publisher : STEKOM PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/elkom.v16i2.1673

Abstract

The global economic recession is a global economic downturn that affects the domestic economies of countries in the world. The stronger the economic dependence of one country on the global economy, the faster a recession will occur in that country. In 2020 the country of Indonesia and even the world are exposed to the COVID-19 virus which has an impact on the country's economic growth, even the world economy. This is the trigger for an economic recession. This has led to many different public perspectives on the occurrence of a global economic recession whose opinions or reactions are expressed on social media Youtube. The data was obtained by crawling techniques from social media Youtube with a total of 500 comments used. The data is then labeled (class) with a lexicon-based method with an Indonesian language dictionary. From the labeling results, it was obtained 185 positive labeled data (37%) and 315 negative opinions (63%). The data preprocessing stage is carried out in preparation for the data to be processed for sentiment analysis. Of the many opinions obtained, an analysis of public sentiment regarding the 2023 global economic recession will be carried out using the Naïve Bayes classification algorithm. This study also applied the TF-IDF word weighting method with the n-gram feature used, namely bigram (n=1). The system will be evaluated using a confusion matrix. The implementation results show a prediction model with a total of 500 opinion data with a comparison of training data and test data of 9:1, producing an accuracy value of 84.00%, a precision value of 75.00%, a recall of 30.00%, and an f1-score of 42.86%. The performance of the system model built in this study can be said to be good.
Penerapan Metode Vikor dalam Pemilihan Bibit Unggul Pohon Karet Rizki Ananda Putra Fajar; Rakhmat Kurniawan; Sriani
Da'watuna: Journal of Communication and Islamic Broadcasting Vol. 4 No. 4 (2024): Da'watuna: Journal of Communication and Islamic Broadcasting (In Press)
Publisher : Intitut Agama Islam Nasional Laa Roiba Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47467/dawatuna.v4i4.1842

Abstract

Rubber plant is one of the plantation commodities that has an important role in economic activities in Indonesia. The need for rubber seeds continues to increase in line with the increase in the area of ​​smallholder rubber plantations and the government. Quality seeds are a community need in developing rubber plantations in Indonesia from year to year. The first step to get good rubber seeds is that rubber farmers need to use quality rubber seed planting material and are able to produce high latex. Given the very importance of seeds in determining quality rubber repair. With a Decision Support System with the vikor method to build a system that has the ability to be able to assist farmers in choosing superior rubber tree seeds with a system that is able to provide problem solving skills and communication skills for problems with semi-structured and unstructured conditions. This study uses the vikor method because this method is suitable for use in most real-time problems such as making decisions to find quality rubber tree seeds so that this research can be useful for rubber tree farmers in improving the quality of superior seeds.
Subject Selection Decision Support System Using the Weighted Aggregated Sum Product Assessment Method Setiawan, Mhd. Liandra; Sriani
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/s2d8vn67

Abstract

High school subject selection is crucial for aligning with students' interests and goals, but manual processes are often time-consuming and prone to errors. This study developed a decision support system using the WASPAS method, which combines WSM and WPM to produce a more stable and consistent evaluation of alternatives. A total of 35 10th-grade students of SMAN 16 Medan were recruited through total sampling using a Likert-scale questionnaire as the basis for the calculation. The system evaluation was verified on the entire data set, not just three samples like the previous version, to ensure the algorithm's suitability. The results show that the system generates interest recommendations based on the highest Qi score and is consistent with manual calculations, although its accuracy cannot yet be fully concluded. The distribution of student preferences is also presented, along with explanations of potential instrument bias and response bias as limitations of the study. Overall, this WASPAS-based system is considered capable of helping provide more objective and efficient subject selection recommendations.
Analisis Sentimen Publik Terhadap Kenaikan Pajak Pertambahan Nilai (PPN) Sebesar 12% Menggunakan Naïve Bayes Classifier Lu'luil Jannah; Sriani
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 7 No. 2 (2025): Desember 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v7i2.9151

Abstract

Perkembangan teknologi digital membuka peluang yang semakin luas bagi masyarakat untuk menyampaikan pendapat secara bebas melalui berbagai platform media sosial. Salah satu isu yang banyak menarik perhatian publik adalah kebijakan pemerintah terkait kenaikan Pajak Pertambahan Nilai (PPN) menjadi 12%. Ragam opini yang muncul dari masyarakat dapat dimanfaatkan sebagai bahan pertimbangan dalam proses evaluasi maupun pengambilan keputusan. Penelitian ini bertujuan untuk mengidentifikasi sentimen publik terhadap kebijakan kenaikan PPN dengan memanfaatkan algoritma Naïve Bayes Classifier. Tahapan penelitian meliputi pengumpulan data dari media sosial, pra-pemrosesan teks seperti case folding, tokenisasi, stopword removal, dan stemming serta pengubahan data teks ke bentuk numerik menggunakan metode TF-IDF. Dari total 600 data yang berhasil dihimpun dari media social X, dengan 80% digunakan sebagai data pelatihan dan 20% data sebagai data pengujian. Hasil penelitian menunjukkan adanya 51 tweet yang bernada positif, 352 bernada netral, dan 197 bernada negatif. Model Naïve Bayes menghasilkan performa klasifikasi yang cukup baik dengan akurasi 81,36%, presisi rata-rata 88%, recall 79%, dan F1-Score 82%. Temuan ini membuktikan bahwa Naïve Bayes merupakan algoritma yang efektif dan layak diandalkan untuk mengklasifikasikan opini publik secara cepat dan sistematis. Dengan demikian, model ini berpotensi menjadi alat pendukung dalam menganalisis persepsi masyarakat terhadap kebijakan pemerintah secara berbasis data.
Penerapan Logika Fuzzy Tsukamoto Sebagai Sistem Pendukung Keputusan Penentuan Mata Kuliah Pilihan Mahasiswa Ilmu Komputer XYZ Muhammad Reza Alhafiz; Sriani
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 1 (2026): Februari 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i1.9453

Abstract

The selection of elective courses poses a challenge for Computer Science students at XYZ University because it influences competency development, while objective decision-making guidance remains limited. This study aims to develop a web-based decision support system to recommend specialization elective courses using the Fuzzy Tsukamoto method. Data were collected through questionnaires from students in semesters five to seven and processed into four input variables: Robotics, Mathematics, Programming, and Analysis. Each variable was modeled into three fuzzy sets (Weak, Moderate, Strong) using trapezoidal membership functions and processed through IF–THEN rule-based inference with a total of 162 rules. Output values were obtained through weighted average defuzzification to generate course recommendations. System testing was conducted by comparing system outputs with manual calculations and evaluated using the Mean Absolute Percentage Error (MAPE). The results showed a MAPE value of approximately ±0.1096%, indicating that the implementation of the Tsukamoto method in the system is consistent with manual calculations. This study contributes to providing a structured and objective decision support system to assist students in determining elective courses based on their competencies.
Segmentation of Toddlers Based on Nutritional Status Using Agglomerative Hierarchical Clustering with Average Linkage Malid, Abdul; Sriani
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 7 No. 3 (2026): Maret 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v7i3.9598

Abstract

Nutritional status among children under five remains an important public health concern, particularly in developing regions where early detection of growth problems is essential for effective intervention. Conventional nutritional assessments often rely on categorical classifications that may not fully capture variations in anthropometric characteristics among toddlers. This study aims to segment children under five based on nutritional status using the Agglomerative Hierarchical Clustering (AHC) algorithm with the Average Linkage method in the NA-IX-X District, North Labuhanbatu Regency. The study used secondary anthropometric data from 1,452 children obtained from the Aek Kota Batu Public Health Center. Quantitative variables, including body weight, height, and age, were standardized using z-score transformation prior to clustering analysis. The results show that a three-cluster configuration provides the optimal segmentation, with a Silhouette Coefficient value of 0.5154, indicating a moderate clustering structure. Cluster 1 (n = 180) shows relatively lower anthropometric measurements with an average body weight of 7.3 kg and height of 68.3 cm. Cluster 2 (n = 511) represents intermediate measurements with an average body weight of 11.5 kg and height of 87.8 cm, while Cluster 3 (n = 761) reflects higher measurements with an average body weight of 15.0 kg and height of 101.7 cm. Dendrogram analysis indicates that a cutting point at height = 1.5 produces the most interpretable cluster separation. These findings demonstrate that hierarchical clustering can support more targeted nutritional intervention strategies at the community health center level. 
Co-Authors Abdul Halim Hasugian Agustin Mardiana, Nur Agustina, Chaterine Aidil Halim Lubis Aidil Halim Lubis Andriyadi, Andriyadi Anggraini, Chintya Armansyah Berutu, Nurhalimah Desmarini, Mutia Dwitia Ayu Lestari Fachry Abda El Rahman Fawi, Hafiz Frianda Gultom, Irsan Gusrianti, Rita Harahap, Lailan Sofinah Harahap, Ulil Amri Harahap, Yunus Fadillah Ibnu Rusydi Ibsan, Muhammad Hanafi juraidah, juraidah Khairani, Fuji Khairunnisa Lu'luil Jannah Mahmudah, Nur Aini Malid, Abdul Mawaddah Mawaddah, Mawaddah Maya Nurmalasari Mona Novita Muhammad Alwi Baihaqi Muhammad Reza Alhafiz Nasution, Yurika Nur Aisyiyah, Siti R Panjaitan, Nurhalimah Pasaribu, Nur Adha Pohan, Rifani Khairani Rahman Sari, Arfani Rakhmat Kurniawan R Rizki Ananda Putra Fajar Setiawan, Mhd. Liandra Shania perida Br tarigan Simanulang, Ester Sinaga, Siti Nurmawan Siti Zubaidah Sofiah Sri Wahyuni Suhardi Tarigan, Eka Falentina Zoyasri Ananta