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All Journal Jurnal Informatika dan Teknik Elektro Terapan JIEET (Journal of Information Engineering and Educational Technology) JOURNAL OF APPLIED INFORMATICS AND COMPUTING METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Computer Science and Information Technology (CoSciTech) International Journal of Engineering, Science and Information Technology Jurnal Informatika dan Teknologi Komputer ( J-ICOM) Jurnal Minfo Polgan (JMP) Sisfo: Jurnal Ilmiah Sistem Informasi Jurnal Teknologi Terapan and Sains 4.0 Jurnal Informatika Polinema (JIP) Jurnal Malikussaleh Mengabdi Journal of Advanced Computer Knowledge and Algorithms Gameology and Multimedia Expert
Cut Agusniar
Universitas Malikussaleh
Religion Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Implementasi Metode TOPSIS Untuk Pemilihan Konten Berkualitas Pada Konten Edukasi Berdasarkan Multi Kriteria Qasos, Lailil; b, bustami; Agusniar, Cut
Jurnal Informatika dan Teknologi Komputer (J-ICOM) Vol 6 No 1 (2025): Jurnal Informatika dan Teknologi Komputer ( J-ICOM)
Publisher : E-Jurnal Universitas Samudra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55377/j-icom.v6i1.11186

Abstract

The rapid development of digital technology has increased the production of educational content on social media platforms, including TikTok. However, the abundance of available content makes it difficult for content creators to select high-quality references. Therefore, this study develops a decision support system using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method to evaluate and rank the best educational videos. This study utilizes 200 educational videos from TikTok, assessed based on seven criteria, namely Hook, visual quality, audio quality, content relevance, text narration, duration, and the number of Likes. Each video is weighted based on expert interviews and analyzed using the TOPSIS method to generate the best content rankings. The results show that the video with the highest score of 0.89612 is the most suitable educational content according to the quality standards, while the video with the lowest score of 0.158256 has lower quality. With this system, content creators can more efficiently design engaging, informative, and educationally impactful content strategies.
Sentiment Analysis of E-Commerce Product Reviews on Tokopedia Using Support Vector Machine Alaiya, Azna; Nurdin, Nurdin; Agusniar, Cut
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10977

Abstract

This research aims to analyze the performance of Support Vector Machine (SVM) algorithm in classifying sentiment of e-commerce product reviews on the Tokopedia platform using web scraping data of 571 reviews from the 2024 period. The data includes review text variables, publication dates, and usernames processed through text preprocessing (text cleaning, stopword removal, stemming with Sastrawi), auto-labeling using a lexicon-based approach, and TF-IDF feature extraction with optimal parameters (max_features=5000, ngram_range=(1,2)) resulting in 1,187 features. Data splitting was performed using stratified method with proportions of training (80%) and testing (20%) on 461 reviews from binary classification filtering (positive vs negative). The research results demonstrate that Support Vector Machine with linear kernel achieved excellent performance with accuracy 95.70%, precision 95.89%, recall 95.70%, and F1-score 94.89% on the testing set. Despite the imbalanced dataset characteristics (92.4% positive vs 7.6% negative), SVM effectively handled the classification task by identifying negative sentiment with 100% precision and 42.86% recall, demonstrating its robustness in handling skewed data distribution. TF-IDF feature analysis identified the highest discriminative words such as "suitable", "goods", and "good" that are relevant for classifying consumer sentiment towards e-commerce products. The results indicate that SVM algorithm is highly effective for sentiment classification of e-commerce product reviews, making it suitable for practical implementation in automated sentiment analysis systems for online marketplaces.
PENERAPAN METODE DEMPSTER SHAFER PADA SISTEM PAKAR UNTUK DIAGNOSIS STUNTING Nurdin, Nurdin; Cesilia, Yolinda; Agusniar, Cut
Jurnal Informatika dan Teknik Elektro Terapan Vol. 13 No. 3S1 (2025)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v13i3S1.8074

Abstract

Stunting is one of the chronic nutritional problems that affects physical growth, cognitive development, and human productivity in the future. This condition is caused by prolonged nutritional deficiencies and health issues during the early stages of life. This study aims to develop an expert system for diagnosing stunting in toddlers using the Dempster Shafer method, which assists medical personnel in performing early detection based on symptoms and expert belief levels. The Dempster Shafer approach is applied due to its ability to handle uncertainty in data and combine multiple pieces of evidence to produce a rational diagnostic conclusion. The research data were obtained from the Posyandu in Babul Makmur District, Southeast Aceh Regency, consisting of 30 test data samples. The system was developed using the Python programming language, Flask framework, and SQLite database. The testing results show that the system achieved an accuracy rate of 36.66%, with 11 out of 30 test data correctly classified according to expert diagnosis. Although the accuracy remains low, this study demonstrates the potential of the Dempster Shafer method as a foundation for evidence-based diagnostic systems in stunting detection.
Pendampingan Sistem Infomasi Profil Berbasis Website Pada Cabang Dinas Pendidikan wilayah Bireun Ula, Mutammimul; Agusniar, Cut; Yurni, Irma; Erliana, Cut Ita; Muhammad, Muhammad; Suryati, Suryati
Jurnal Malikussaleh Mengabdi Vol. 3 No. 1 (2024): Jurnal Malikussaleh Mengabdi, April 2024
Publisher : LPPM Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jmm.v3i1.16694

Abstract

Sistem Informasi Profil Cabang Dinas Pendidikan Wilayah Kabupaten Bireuen masih menggunakan profil biasa yang belum maksimal penggunaan nya. Pihak pengguna dalam melihat/mengunjungi website dalam melihat informasi terbaru dan hal tersebut mengakibatkan memerlukan waktu yang lama dalam mengakses informasi tersebut. Tujuan pengabdian ini adalah untuk mengembangkan Sistem Informasi Profil Berbasis Website pada Cabang Dinas Pendidikan Wilayah Kabupaten Bireuen guna dalam meningkatkan efisiensi dan akurasi  informasi dalam pengelolaan informasi data pendidikan. Cabang Dinas Pendidikan saat ini menghadapi tantangan dalam pengelolaan data profil sekolah dan tenaga pendidik dalam melihat informasi tersebut, sehingga sulit diakses dengan cepat dan rentan terhadap kehilangan data serta ketidakakuratan informasi. Adanya pengembangan sistem berbasis website ini diharapkan dapat mempercepat akses data, mengurangi risiko kehilangan data dan pengelolaan data. Manfaat dari pengabdian ini diharapkan berupa pengelolaan data yang lebih efisien dan akurat, kemudahan akses data bagi masyarakat, serta peningkatan kualitas layanan pendidikan melalui data real-time yang mendukung pengambilan keputusan yang lebih baik. Hasil dari pengabdian ini terintegrasi dan dapat diakses secara system oleh berbagai pihak dengan hak akses yang disesuaikan. Selanjutnya hasil yang diharapkan berupa sistem informasi profil berbasis website yang efektif dan efisien, peningkatan kapasitas pengguna melalui pendampingan, serta dokumentasi dan panduan penggunaan sistem.
Penerapan Logika Fuzzy Tsukamoto pada Rancang Bangun Sistem Deteksi Kekeruhan Air Budi Daya Ikan Lele Rizki, Muhammad; Darnila, Eva; Agusniar, Cut
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 9 No. 1 (2025): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol9No1.pp112-120

Abstract

This study develops a water quality monitoring system for catfish farming using the Internet of Things (IoT) and Fuzzy Tsukamoto logic. This system consists of a Turbidity Sensor to measure turbidity levels, a DS18B20 sensor to monitor temperature, and a pH meter to measure water acidity levels. Data from the sensors is sent in Realtime to Firebase and displayed in an Android application based on Kodular. The Fuzzy Tsukamoto method is used to analyze data, determine the water quality status whether the water value is Clean, Normal, or Turbid based on predetermined parameters. Based on 14 tests, the system showed an accuracy level of 85.7%, with 12 matching results. In addition, this system is able to provide automatic notifications to users if there are significant changes in water conditions. As a result, this system can help fish farmers monitor water quality efficiently, as well as make decisions about when is the right time to change pond water.
A Comparative Study of K-Means and K-Medoids for Clustering Dengue Fever Risk Areas in Medan Fitri, Anisa Amelia; Ula, Munirul; Agusniar, Cut
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 6 No. 4 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v6i4.8702

Abstract

Dengue Hemorrhagic Fever (DHF) is a localized disease that continues to contribute to a high number of cases in Medan City. The local health authority faces challenges in identifying priority areas for effective prevention and control. This study applies data clustering techniques to map DHF risk areas by comparing the performance of K-Means and K-Medoids algorithms. The optimal number of clusters was determined using the Silhouette Coefficient, while the clustering quality was assessed using the Davies-Bouldin Index (DBI). The findings indicate that K-Means performs best with four clusters and achieves a lower DBI value compared to K-Medoids. Based on this, the study recommends using K-Means to categorize DHF risk areas into four priority levels: high, medium, low, and very low. This approach is expected to support the Medan City Health Office in implementing more targeted and efficient DHF control strategies.
Co-Authors Alaiya, Azna Aryanti, Leni Asran Asran b, bustami Cesilia, Yolinda Cut Ita Erliana Dahlan Abdullah Dalimunthe, Rodiatul Adawiyah Della Adelia, Della Eva Darnila Ezwarsyah Ezwarsyah Fazira, Ira Fidyatun Nisa Fitri, Anisa Amelia Gemasih, Husna Ginting, Andriyan Herfia Rhomadhona Khaira, Raziatul laili wahyunita, laili Maryana Mochamad Ari Saptari Muhammad Muhammad Muhammad Rizki Mulya Ulfa, Septia Munauwarah, Munauwarah Munirul Ula Muslimah Siregar, Khanifa Mutammimul Ula Muthmainnah Muthmainnah Nisa Ul Fadila Nurdin Nurdin Qasos, Lailil Rahma Fitria, Rahma Ramadan, Rifki Rasmiati Rasyid Rini Meiyanti Rizal S.Si., M.IT, Rizal Safwandi Safwandi Sahputra, Ilham Sinambela, Ilmi Suciani Sujacka Retno Sukiman, T. Sukma Achriadi Suryati Suryati Syibral Malasyi Syukriah Syukriah, Syukriah Ulfa, Nur Saufani Veri Ilhadi Yurni, Irma Zubaedah, Reza Zuraida