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All Journal JOIV : International Journal on Informatics Visualization International Journal of Artificial Intelligence Research PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer JURNAL PENDIDIKAN TAMBUSAI Journal of Information Technology and Computer Engineering JURTEKSI Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal Review Pendidikan dan Pengajaran (JRPP) Jusikom: Jurnal Sistem Informasi Ilmu Komputer JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Infortech Jurnal Pendidikan Guru (JPG) Journal of Applied Data Sciences Journal of Computer Scine and Information Technology Bulletin of Computer Science Research Jurnal Pustaka Data : Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitektur Komputer Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence Jurnal Komtekinfo SmartComp Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
Sovia, Rini
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Social Sciences Other

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Journal : JOURNAL OF SCIENCE AND SOCIAL RESEARCH

 1 
KLASIFIKASI CITRA DALAM IDENTIFIKASI KOL DAN WORTEL MENGGUNAKAN ALGORITMA LDA DAN KNN Nurdiansyah, Ali; Erlanda, Hadrian; Syafril, Syafril; Roza, Yesi Betriana; Sovia, Rini
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 8, No 2 (2025): May 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i2.2894

Abstract

Abstract: Agriculture is an important sector in the Indonesian economy, where vegetables such as cabbage (Brassica oleracea var. capitata) and carrots (Daucus carota subsp. sativus) play a significant role in meeting the nutritional needs of the community. With the increasing demand for fresh vegetable products, it is important to ensure accurate and efficient identification of these types of vegetables. Mistakes in identification can result in economic losses and affect the quality of products reaching consumers. Image processing technology and machine learning algorithms offer promising solutions to this problem. Image classification, which involves visual analysis of vegetable images, can be used to identify species based on features extracted from the image. Based on these problems, researchers are interested in conducting research on image classification of 2 types of vegetables, namely cabbage and carrots using the KNN and LDA algorithms. From this system, the accuracy results of the classification of green cabbage, purple cabbage and carrots using the KNN and LDA methods were 92.8571%. This research is expected to provide new insights into the use of modern technology to support the preservation and utilization of vegetable types and sustainability. Keyword: Hybrid Intelligence System; Vegetable Classification; Image Processing; LDA; KNN Abstrak: Pertanian merupakan sektor penting dalam perekonomian Indonesia, di mana sayuran seperti kubis (Brassica oleracea var. capitata) dan wortel (Daucus carota subsp. sativus) memiliki peran signifikan dalam memenuhi kebutuhan gizi masyarakat. Dengan meningkatnya permintaan akan produk sayuran segar, penting untuk memastikan identifikasi yang akurat dan efisien terhadap jenis-jenis sayuran ini. Kesalahan dalam identifikasi dapat mengakibatkan kerugian ekonomi dan mempengaruhi kualitas produk yang sampai ke konsumen. Teknologi pemrosesan citra dan algoritma pembelajaran mesin menawarkan solusi yang menjanjikan untuk masalah ini. Klasifikasi citra, yang melibatkan analisis visual dari gambar sayuran, dapat digunakan untuk mengidentifikasi spesies berdasarkan fitur-fitur yang diekstraksi dari citra tersebut. Berdasarkan permasalahan tersebut maka peneliti tertarik untuk melakukan penelitian mengenai klasifikasi citra 2 jenis sayuran yaitu kol dan wortel menggunakan algoritma KNN dan LDA. Dari sistem tersebut didapatkan hasil akurasi dari klasifikasi jenis sayur kol hijau, kol ungu dan wortel menggunakan metode KNN dan LDA sebesar 92.8571 %. Penelitian ini diharapkan dapat memberikan wawasan baru dalam penggunaan teknologi modern untuk mendukung pelestarian dan pemanfaatan jenis sayur dan berkelanjutan. Kata kunci: Hybrid Intelligence System; Klasifikasi Sayur; Pengolahan Citra; LDA; KNN
IDENTIFIKASI TINGKAT KEMATANGAN BUAH MANGGA MENGGUNAKAN METODE K-MEANS CLUESTERING DAN MEDIAN FILTER Yanti, Rahma; Yasmin, Nabilla; Putra, Kharisma Utama; Irawan, Hendri; Sovia, Rini
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 8, No 2 (2025): May 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i2.2893

Abstract

Abstract: This study aims to develop an automatic system for identifying the ripeness level of mangoes using the K-Means Clustering and Median Filter methods. The background of this research is based on the agricultural industry's need for an objective ripeness assessment, as manual methods are often subjective and inefficient. The K-Means Clustering method is used to categorize mango ripeness based on skin color characteristics, while the Median Filter is applied to enhance image quality by reducing noise before clustering. This study utilizes a dataset of 120 mango images, consisting of 47 images for training and 73 images for testing. The results indicate that the combination of these two methods achieves a classification accuracy of 98%. These findings contribute to the development of digital image processing technology for applications in the agricultural and food industries. Keyword: Ripeness identification, K-Means Clustering, Median Filter, Image Processing, Mango. Abstrak: Penelitian ini bertujuan untuk mengembangkan sistem identifikasi tingkat kematangan buah mangga secara otomatis menggunakan metode K-Means Clustering dan Median Filter. Latar belakang penelitian ini didasarkan pada kebutuhan industri pertanian dalam menentukan tingkat kematangan mangga secara objektif, mengingat metode manual sering kali subjektif dan kurang efisien. Metode K-Means Clustering digunakan untuk mengelompokkan tingkat kematangan mangga berdasarkan karakteristik warna kulit, sedangkan Median Filter diterapkan untuk meningkatkan kualitas citra dengan mengurangi noise sebelum dilakukan proses klasterisasi. Penelitian ini menggunakan dataset sebanyak 120 citra mangga, yang terdiri dari 47 citra untuk pelatihan dan 73 citra untuk pengujian. Hasil penelitian menunjukkan bahwa kombinasi kedua metode ini mampu mengklasifikasikan tingkat kematangan mangga dengan akurasi sebesar 98%. Temuan ini memberikan kontribusi dalam pengembangan teknologi pemrosesan citra digital untuk aplikasi dalam industri pertanian dan pangan. Kata kunci: Identifikasi kematangan, K-Means Clustering, Median Filter, Pengolahan Citra, Mangga.
IMPLEMENTASI ALGORITMA FUZZY UNTUK PENILAIAN KEPUASAN NASABAH PNM MEKAR DI PASAMAN Yanti, Rahma; Ramadani, Sela; Selvia, Dina; Sovia, Rini
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 8, No 4 (2025): November 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i4.4849

Abstract

Customer satisfaction assessment is an essential component in improving the service quality of PNM Mekar, a microfinance institution focused on empowering women through ultra-micro financing. Conventional evaluations rely heavily on subjective perceptions, creating a need for a more structured and objective method. This study applies the Fuzzy Logic algorithm to measure customer satisfaction by transforming numerical data into linguistic variables through fuzzification. Annual operational data, including the number of customers and returning customers, were processed using membership functions and fuzzy rules, followed by defuzzification to obtain a crisp satisfaction value. The results indicate that all satisfaction levels fall into the low category, suggesting the need for service improvement. The fuzzy-based model proves effective in providing adaptive, consistent, and realistic satisfaction evaluation.
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.
ANALISIS KUALITAS PRODUKSI AYAM BROILER MENGGUNAKAN METODE K-MEANS CLUSTERING DAN ALGORITMA C4.5 Saputra, Oriza Rama; Sovia, Rini; Yanto, Musli
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.v9i1.5747

Abstract

Abstract: Broiler chicken production is influenced by various factors, such as feed, environment, and maintenance management, which generate large amounts of complex production data. This condition causes the assessment and decision-making processes related to production quality to often be suboptimal and not based on in-depth data analysis. This study aims to implement the K-Means method and C4.5 algorithm to produce an analysis process that can be used as a solution in determining broiler chicken production quality in the South Coast region. The K-Means method is used to classify broiler chicken production data based on similar characteristics to facilitate pattern identification. The C4.5 algorithm was used to build a decision tree model to determine and predict broiler chicken production quality based on the most influential attributes. The research dataset was sourced from farm data in the South Coast region, with a total of 157 data points obtained. The clustering results presented three main segments, namely 94 with good results, 58 data with moderate results, and 5 data with poor results. Meanwhile, the C4.5 algorithm was built based on the clustering results from K-Means. The accuracy was calculated using the F1 score, with an accuracy of 93,75%. Keywords: Broiler Chicken;K-Means; C4.5 Algorithm. Abstrak: Produksi ayam broiler dipengaruhi oleh berbagai faktor, seperti pakan, lingkungan, dan manajemen pemeliharaan, yang menghasilkan data produksi dalam jumlah besar dan bersifat kompleks. Kondisi ini menyebabkan proses penilaian dan pengambilan keputusan terkait kualitas produksi sering kali belum optimal dan kurang didasarkan pada analisis data yang mendalam. Penelitian dilakukan bertujuan untuk Mengimplementasikan metode K-Means dan algoritma C4.5 untuk menghasilkan proses analisis yang dijadikan solusi dalam penentuan kualitas produksi ayam broiler di wiliyah Pesisir Selatan. Metode K-Means digunakan untuk mengklasifikasikan data produksi ayam broiler berdasarkan kesamaan karakteristik untuk memudahkan identifikasi pola. Algoritma C4.5 Digunakan untuk membangun model pohon keputusan dalam menentukan dan memprediksi kualitas produksi ayam broiler berdasarkan atribut yang paling berpengaruh. Dataset penelitian bersumber dari data peternakan di wilayah Pesisir Selatan, Dengan total data ada 157 data yang didapatkan. Hasil klustering menyajikan tiga segmen utama yaitu 94 dengan hasil baik, 58 data dengan hasil sedang dan 5 data dengan hasil buruk, Sedangkan Algoritma C4.5 dibangun berdasarkan hasil klustering dari K-Means. Perhitungan Hasil akurasi dengan f1 score Dengan hasil akurasi 93,75%. Kata Kunci: Ayam Broiler, K-Means, Algoritma C4.5
Co-Authors Adinda Syalsabila Aditra Agus Salim, David Al-arrafi, Muhammad Ikhsan Amin Amirul Mukminin, Andi Anam, M Khairul Awal, Hasri Borianto, B Chairunnissa Deliva Akbar, Syifa Devia Kartika Dila, Rahmah Effendy, Geraldo Revanska Elmi Rahmawati, Elmi Erlanda, Hadrian Fana, Wulan Stau Fatimah, Noor Gema, Rima Liana Gunadi Widi Nurcahyo Gusriva, Revi Hadi, Aulia Fitrul Hadiyanto, Tegas Harnaranda, Jefri Hartika Zain, Ruri Hartika Hendri Irawan Hendrik, Billy Huda, Ramzil Irzal Arief Wisky Islam, Md Ataul Khomsi, Ahmad Lubis, Fitri Amelia Sari maha rani Nugraha, Fajri Nurdiansyah, Ali Nursyahrina Permana, Randi Permana, Randy Putra, Kharisma Utama Rahmad Rahmad Rahman, Muhammad Aidil Rahman, Zumardi Rahmi, Nadya Alinda Ramadani, Sela Ramadhanu, Agung Renaldi, Encik Yoega Ricki Ardiansyah, Ricki Ridwan Sutri Rinaldi Chan, Fajri Rindhani Aditia, Mellya Riyan Saputra Riyan Saputra, Riyan Roza, Yesi Betriana Rozakh, Muhammad Ruri Hartika Zain S, Sumijan Sandawa, Hoka Muhgrah Saputra, Charisman Fajri Saputra, Oriza Rama Saputra, Randy Sarjon Defit Selfi Melisa Selvia, Dina Siregar, Diffri Sumijan Sumijan Sumijan Sutri, Ridwan Syafril Syafril Syaiffullah, Afif Tika Christy Wahyudi, Anggy Wirdawati, Wira Yanti, Rahma Yanto, Musli Yasmin, Nabilla Yenila, Firna Yuhandri Yuhandri Yuhandri, Yuhandri Yuhandri, Y