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All Journal Techno.Com: Jurnal Teknologi Informasi JOIV : International Journal on Informatics Visualization International Journal of Artificial Intelligence Research Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Ilmiah Media Sisfo JOURNAL OF SCIENCE AND SOCIAL RESEARCH JOISIE (Journal Of Information Systems And Informatics Engineering) INTI Nusa Mandiri Jurnal Ekonomi Manajemen Sistem Informasi Jurnal Teknologi Dan Sistem Informasi Bisnis JATI (Jurnal Mahasiswa Teknik Informatika) Indonesian Journal of Electrical Engineering and Computer Science Jurnal Pendidikan Guru (JPG) Bulletin of Computer Science Research JITSI : Jurnal Ilmiah Teknologi Sistem Informasi Journal of Education Research Algoritme Jurnal Mahasiswa Teknik Informatika Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence Jurnal Hasi Penelitian Dan Pengkajian Ilmiah Eksakta - JPPIE Jurnal Ekonomika Dan Bisnis Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Sains dan Teknologi Jurnal Komtekinfo Indonesian Journal Computer Science (ijcs) Journal of Information System and Education Development Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) The Indonesian Journal of Computer Science CSRID
Ramadhanu, Agung
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Penerapan Image Processing untuk Identifikasi RAM, SSD, dan Webcam Menggunakan Metode K-Means Clustering Hikmi, Zakiya; Ramadhanu, Agung
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol 8 No 1 (2026): Januari 2026
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v8i1.2266

Abstract

The development of computer hardware requires appropriate automatic identification methods to assist in inventory, maintenance, and learning processes. Manual identification methods for hardware such as RAM, SSD, and webcams are often ineffective due to the difficulty of distinguishing their visual forms, especially for those who are unfamiliar with them. This study aims to apply image processing techniques using the K-Means clustering method to identify these three types of devices. The system was created using MATLAB with a graphical user interface (GUI) for ease of use. The process begins by capturing images in RGB format, which are then converted to Lab* color space. Segmentation is performed using the K-Means clustering method, which divides objects from the background into two clusters. The segmentation results are then refined using morphological operations. Next, shape features and texture features are extracted using Gray Level Co-occurrence Matrix (GLCM), which includes contrast, correlation, energy, and homogeneity. The features obtained are compared with the database using Euclidean distance to determine the type of hardware. The test results show that the system is able to accurately distinguish between RAM, SSD, and webcams. In conclusion, the use of K-Means clustering, GLCM, and distance-based classification can be an effective solution in identifying computer hardware through images.
Implementasi Metode K-Means Clustering untuk Mengklasterikasikan Perangkat Elektronik dengan Teknik Pengolahan Citra Firmansyah, Ryan; Ramadhanu, Agung
Jurnal Penelitian Dan Pengkajian Ilmiah Eksakta Vol 5 No 1 (2026): Jurnal Hasi Penelitian Dan Pengkajian Ilmiah Eksakta - JPPIE
Publisher : LPPM Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jppie.v5i1.2270

Abstract

Grouping electronic devices such as computers, laptops, and smartphones will be very useful in situations where there are a large number of devices to manage, for example in companies, schools, or service centers. This study uses the k-means clustering method with image processing techniques through the Matlab application. The test data used was taken from the internet, consisting of 30 samples comprising 10 computers, 10 laptops, and 10 smartphones. In accordance with the existing dataset, clustering will be performed on three types of electronic devices, namely computers, laptops, and smartphones. After conducting various tests and model designs, the overall accuracy of the model is 100%. This research can cluster 30 samples consisting of 10 computer images, 10 laptop images, and 10 smartphone images. All samples used were taken from the internet.
ANALISIS SENTIMEN MASYARAKAT MENGGUNAKAN ALGORITMA NAÏVE BAYES DAN SUPPORT VECTOR MACHINE TERHADAP PROGRAM BPJS Saputra, Charisman Fajri; Sovia, Rini; Ramadhanu, Agung
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 9, No 1 (2026): February 2026
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i1.5660

Abstract

Abstract: BPJS Kesehatan is a national health insurance program that plays a vital role in providing public health services in Indonesia; however, its implementation has generated diverse public perceptions reflected on social media. This study analyzes public sentiment toward the BPJS Kesehatan program based on Instagram comments using a text mining and machine learning approach. The research methodology includes Indonesian text preprocessing, feature weighting using Term Frequency–Inverse Document Frequency (TF–IDF), and three-class sentiment classification (positive, negative, and neutral) using Multinomial Naïve Bayes and Support Vector Machine (SVM) algorithms. The dataset consists of 1,461 Instagram comments, which are divided into training and testing data with an 80:20 ratio. The experimental results show that Multinomial Naïve Bayes achieves an accuracy of 80.55%, while SVM yields a higher accuracy of 86.35%. These results indicate that SVM performs better in separating sentiment classes within short and imbalanced Instagram comment data. This study contributes to Indonesian-language sentiment analysis research and provides insights for evaluating public health services through social media data. Keyword: sentiment analysis; BPJS Kesehatan; Instagram; Naïve Bayes; Support Vector Machine. Abstrak: BPJS Kesehatan merupakan program strategis nasional yang berperan penting dalam menjamin akses layanan kesehatan bagi masyarakat Indonesia, namun implementasinya masih memunculkan beragam persepsi publik yang tercermin pada media sosial. Penelitian ini mengkaji analisis sentimen masyarakat terhadap program BPJS Kesehatan berdasarkan komentar pada platform Instagram menggunakan pendekatan text mining dan pembelajaran mesin. Metode penelitian meliputi pra-pemrosesan teks berbahasa Indonesia, pembobotan fitur menggunakan Term Frequency–Inverse Document Frequency (TF–IDF), serta klasifikasi sentimen tiga kelas (positif, negatif, dan netral) menggunakan algoritma Multinomial Naïve Bayes dan Support Vector Machine (SVM). Dataset yang digunakan terdiri dari 1.461 komentar Instagram yang dibagi menjadi data latih dan data uji dengan rasio 80:20. Hasil pengujian menunjukkan bahwa Multinomial Naïve Bayes menghasilkan akurasi sebesar 80,55%, sedangkan SVM mencapai akurasi yang lebih tinggi yaitu 86,35%. Temuan ini menunjukkan bahwa SVM memiliki kemampuan yang lebih baik dalam memisahkan kelas sentimen pada data komentar Instagram yang bersifat pendek dan tidak seimbang. Penelitian ini diharapkan dapat memberikan kontribusi dalam pengembangan analisis sentimen berbahasa Indonesia serta menjadi masukan awal bagi evaluasi layanan publik berbasis media sosial. Kata kunci: analisis sentimen; BPJS Kesehatan; Instagram; Naïve Bayes; Support Vector Machine.
Co-Authors Afriadi Afriadi Afriadi, A Agsera, Nilam Agus Salim, David Agusty, Dhia Fadhila Ahmad Syarif ahmad yani Akbar, Syifa Chairunnissa Deliva Al-arrafi, Muhammad Ikhsan Angga Angga Anggara Putra, Febri Antoni Antoni atiqah, sri Bayuputra, Ramdani Chairunnissa Deliva Akbar, Syifa Chan, Fajri Rinaldi Delvi, Syerlin Aprilia Desi Permata Sari Devita, Retno Dicky Imansyah, Muhammad Dila, Rahmah Dinantia, Triend Enggari, Sofika Erlanda, Hadrian Fajri Saputra, Charisman Firmansyah, Ryan Gafari, Abuzar Gunadi Widi Nurcahyo Hadi Syahputra Halifia Hendri Harnaranda, Jefri Hendri, Hallifia Hidayati, Dzil Hidayattullah, Hafis Hikmi, Zakiya Honestya, Gabriela Ilmawan, Fachrul Irsyad, As'Ary Sahlul Kareem, Shahab Wahhab Karseno, Doni Khomsi, Ahmad Maharani, Filsha Rifi Majid, Mazlina Abdul Mardison Mardison Masri, Taufik Muhammad Idris Muhammad Yusuf Nadia, Nadia Aini Hafizhah Negoro, Wahyu Saptha Ningsih, Neni Sri Wayuni Nurdiansyah, Ali Nurjannah, Farah Permata, Edo Pertiwi, Yuliana Pratama, Dede Putra, Kharisma Utama putri, kamila amaliah Rahmad Rahmad Rahmad, R Rianti, Eva Riati, Itin Riyan Saputra, Riyan Rosa, Imelda Sajida, Mayang salim, alfajri Saputra, Charisman Fajri Saputra, Randy Sarjon Defit Selvia, Dina Silfia Andini, Silfia Sovia, Rini Sumijan, S Sutri, Ridwan Syafri Arlis Syafril Syafril Syafril, S Syalsabilla, Adinda Tesa Vausia Sandiva Utama Putra, Kharisma Vidyanti, Angela Citra Wiratama, Aditya Wirdawati, Wira Yanti, Rahma Yasmin, Nabila Yasmin, Nabilla Yemi, Leonardo Yesi Betriana Roza, yesibetriana_18 Yolanda Yolanda, Yolanda Yosfand, Windra Yuhandri Yulihartati, Sandra Zubaidah, Rima Puti