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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Sisfokom (Sistem Informasi dan Komputer) Jiko (Jurnal Informatika dan komputer) Conference on Innovation and Application of Science and Technology (CIASTECH) Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) JURIKOM (Jurnal Riset Komputer) Jurnal Teknologi Terpadu Building of Informatics, Technology and Science Technologia: Jurnal Ilmiah Jusikom: Jurnal Sistem Informasi Ilmu Komputer Jurnal Tekinkom (Teknik Informasi dan Komputer) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) TIN: TERAPAN INFORMATIKA NUSANTARA Journal of Computer Networks, Architecture and High Performance Computing Jurnal Teknik Informatika (JUTIF) Jurnal Pendidikan dan Teknologi Indonesia J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Informatika Teknologi dan Sains (Jinteks) Innovative: Journal Of Social Science Research VIDHEAS: Jurnal Nasional Abdimas Multidisiplin Scientific Journal of Informatics
Rohana, Tatang
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KLASIFIKASI JENIS BUAH TOMAT MENGGUNAKAN ALGORITMA K-NEAREST NEIGHBOR DAN SUPPORT VECTOR MACHINE Niendha Biell Binna; Rohana, Tatang; Novita, Hilda Yulia; Faisal, Sutan
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 7 No 2 (2025): EDISI 24
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v7i2.5743

Abstract

Tomat adalah salah satu jenis komoditas hortikultura yang cukup banyak di Indonesia dengan variasi bentuk, ukuran, dan warna. Pemilahan jenis tomat secara manual oleh petani maupun pedagang masih memiliki kelemahan, seperti keterbatasan fisik dan ketidakkonsistenan dalam klasifikasi. Tujuan dari penelitian ini adalah untuk mengembangkan sistem klasifikasi untuk jenis tomat. berbasis citra digital menggunakan metode Support Vector Machine (SVM) dan K-Nearest Neighbor (KNN). Proses klasifikasi dilakukan dengan mengekstraksi fitur warna dari kanal Hue (HSV), tekstur dari Gray-Level Co-occurrence Matrix (GLCM), dan bentuk dari kontur objek tomat. Dataset terdiri dari tiga jenis tomat, yaitu tomat ceri, tomat hijau, dan tomat sayur. Data melalui tahapan pra-pemrosesan sebelum dilakukan pelatihan dan pengujian model. Hasil evaluasi menunjukkan bahwa algoritma KNN menghasilkan akurasi 94,44%, sedangkan SVM mencapai akurasi sempurna sebesar 97,22%. Evaluasi dilakukan menggunakan metrik confusion matrix, akurasi, precision, recall, dan F1-score. Temuan ini mengindikasikan bahwa SVM memiliki keunggulan dalam mengklasifikasikan jenis tomat secara akurat, serta menunjukkan potensi besar penerapan teknologi klasifikasi citra dalam mendukung efisiensi proses pertanian modern
KLASTERING SPESIFIKASI DAN HARGA SMARTPHONE MENGGUNAKAN METODE FUZZY C-MEANS DAN PCA Butar Butar, Naomi Nova Meylica; Mudzakir, Tohirin Al; Novita, Hilda Yulia; Rohana, Tatang
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 7 No 2 (2025): EDISI 24
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v7i2.5748

Abstract

Perkembangan pengguna smartphone yang pesat di Indonesia menuntut pendekatan segmentasi pasar yang lebih akurat, terutama terkait harga dan spesifikasi. Penelitian ini mengusulkan model klasterisasi menggunakan kombinasi Principal Component Analysis (PCA) dan Fuzzy C-Means (FCM) untuk memahami pola dalam data produk smartphone. Dataset diambil dari platform Kaggle dan mencakup berbagai atribut teknis seperti RAM, ROM, harga, baterai, serta fitur tambahan seperti dukungan 5G dan tipe perangkat (PRO/PLUS atau LITE). Melalui PCA, sembilan atribut direduksi menjadi empat komponen utama yang mampu mempertahankan 94% variasi data. FCM kemudian diterapkan untuk membentuk kelompok berdasarkan keanggotaan fuzzy, menghasilkan klasifikasi yang lebih fleksibel dan adaptif terhadap data yang tumpang tindih. Nilai Silhouette Score meningkat dari 0,57 menjadi 0,73 setelah reduksi dimensi, mengindikasikan kualitas pemisahan klaster yang lebih baik. Sebanyak sembilan klaster terbentuk, masing-masing mencerminkan segmen pasar mulai dari kelas entry-level hingga flagship. Hasil ini dapat menjadi acuan dalam perencanaan strategi pemasaran, pengembangan produk, dan pengambilan keputusan bisnis berbasis data.
Road Damage Detection Using Yolov9-Based Imagery Azhari, Febrian Akbar; Rohana, Tatang; Baihaqi, Kiki Ahmad; Fauzi, Ahmad
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 2 (2025): MEY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i2.2377

Abstract

Road damage is one of the leading factors contributing to traffic accidents. Rapid identification and repair of damaged roads are crucial in road infrastructure management. This study aims to develop an effective method for detecting road damage, utilizing the YOLOv9 algorithm as a key component, such as cracks and potholes, using the Convolutional Neural Network (CNN) approach. YOLOv9 was chosen due to its efficient architecture, which enables real-time object detection, and its proven effectiveness in various object detection tasks. An annotated dataset of road images was used during the model training and testing process. The results show that the YOLOv9 model can accurately detect road damage. The model achieved a precision of 0.85 and a recall of 0.992 for pothole detection, and a precision of 0.94 for crack detection. Evaluation using mAP50 yielded a score of 0.96, while mAP50-95 reached 0.77, indicating strong detection and classification capability. A consistent decline in loss functions during training also signifies effective learning by the model. These findings suggest that YOLOv9 has the potential to be implemented in automated road damage detection systems, which can accelerate maintenance processes and enhance road user safety.
Public Sentiment Analysis on the Boycott Israel Movement on Platform X Using Random Forest and Logistic Regression Algorithms Agustin, Rachmayanti Tri; Cahyana, Yana; Baihaqi, Kiki Ahmad; Rohana, Tatang
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9551

Abstract

This research aims to analyze public sentiment toward the boycott movement against Israel on the X platform by applying Random Forest and Logistic Regression algorithms. The study uses 616 tweets collected through web crawling with relevant keywords such as "Boikot", "Israel", and "Palestine", covering the period from March 1, 2023 to January 30, 2025. The dataset underwent preprocessing including cleaning, normalization, stopword removal, tokenization, and stemming. Sentiment labeling was conducted both manually, categorizing the data into positive, negative, and neutral classes. TF-IDF was used for feature weighting. The data was split into 80% training and 20% testing. The Random Forest model achieved an accuracy of 70%, while Logistic Regression reached 68%. Both models showed higher accuracy in predicting positive sentiment compared to negative and neutral. The results suggest that public opinion on the boycott movement on social media tends to be supportive, with “Boikot,” “Israel,” and “Palestine” being the most dominant terms. Random Forest performed slightly better in classification, though improvements are needed in recognizing non-positive sentiments.
Komparasi Algoritma Machine Learning dalam Memprediksi Kapasitas Produksi Potensial Air Bersih di Indonesia Rohana, Tatang; Novita, Hilda Yulia; Nurlaelasari, Euis
Jurnal Teknologi Terpadu Vol 11 No 1 (2025): Juli, 2025
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Clean water availability is a key indicator of sustainable development, particularly in developing countries like Indonesia. Factors such as population growth, climate change, and urbanization contribute to fluctuations in clean water supply. This study aims to estimate the potential for clean water production in Indonesia using various machine learning algorithms, such as Linear Regression, Decision Tree, Random Forest, Multilayer Perceptron, XGBoost (Extreme Gradient Boosting), and Neural Network. Each algorithm was evaluated based on Mean Squared Error (MSE), Mean Absolute Error (MAE), R-squared (R²), and prediction accuracy. The results show that Linear Regression achieved the lowest MSE (9.31E-18), nearly zero, indicating extremely accurate predictions. Neural Network and Multilayer Perceptron also performed well, with MSE values of 0.00010898 and 0.00018004, respectively. Moreover, Linear Regression and Neural Network achieved R² scores of 1 and 0.9905, suggesting they can explain nearly all variability in the target data. These findings highlight the effectiveness of Linear Regression, Neural Network, and Multilayer Perceptron in modeling clean water production capacity. Therefore, these algorithms are recommended as the most reliable approaches for supporting data-driven decisions in clean water resource planning and management in Indonesia.
Perbandingan Algoritma Generalized Linear Model Dan Linear Regression Untuk Prediksi Hujan Berbasis Data Kaggle Irawan, Muhamad Anggi; Juwita, Ayu Ratna; Awal, Elsa Elvira; Rohana, Tatang
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 9 (2025): JPTI - September 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.967

Abstract

Prediksi curah hujan sangat penting bagi berbagai aktivitas yang dipengaruhi kondisi cuaca, khususnya di negara beriklim tropis mengalami kondisi ini secara signifikan. Prediksi curah hujan yang akurat sangat penting untuk mendukung berbagai aspek  perencanaan  kota,  termasuk  pengelolaan  sumber  daya  air  dan  mitigasi  risiko  bencana banjir. Penelitian ini membandingkan dua algoritma machine learning, Generalized Linear Model (GLM) dan Linear Regression, dalam memprediksi curah hujan berdasarkan fitur cuaca seperti suhu, kelembaban, tekanan, angin, tutupan awan, dan data historis. Selanjutnya diproses melalui encoding yang dimana akan mengubah nilai kategorikal menjadi nilai numerik, normalisasi yang melibatkan penyesuaian ulang nilai nilai dalam dataset, dan penanganan class imbalance untuk melakukan duplikasi sample pada kelas minotitas. Setelah dibagi menjadi data latih dan uji, kedua algoritma diterapkan dan dievaluasi menggunakan akurasi, RMSE, dan MAE. Hasilnya, GLM memiliki akurasi sebesar 90.17% lalu untuk RMSE sebesar 0.3949 dan MAE 0.3836, se  dangkan Linear Regression lebih baik dalam nilai MAE sebesar 0.2656  dan RMSE 0.3218 untuk akurasi sebesar 89.26%. Dengan pendekatan analisis yang tepat, pola tersebut dapat dimanfaatkan untuk mendukung keputusan dan perencanaan secara lebih terarah.
Klasifikasi Daun Mangga Yang Terkena Hama Dengan Metode Gray Level Co-occurrence Matrix Menggunakan Support Vector Machine Dan K-Nearest Neighbor Berbasis Data Kaggle Nursyawalni, Reva; Indra, Jamaludin; Rohana, Tatang; Wahiddin, Deden
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 9 (2025): JPTI - September 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.1009

Abstract

Penurunan produksi buah mangga di sebabkan oleh kerusakan atau serangan hama pada daun mangga ada beberapa jenis hama pada daun mangga yang umum menyerang antara lain kutu daun (Aphis gossypii), bercak daun alternaria, anthracnose, penggerek batang dan lain-lain. Untuk memperoleh hasil klasifikasi yang lebih akurat dan performa model yang optimal, dibutuhkan sistem yang mampu menghasilkan tingkat akurasi terbaik. Sebagai respons terhadap urgensi tersebut, penelitian ini bertujuan untuk mengklasifikasikan daun mangga yang terkena hama dengan memanfaatkan algoritma Support Vector Machine dan K-Nearest Neighbor, serta penggunaan Gray Level Co-occurrence Matrix sebagai metode untuk mengekstraksi tekstur gambar. Rangkaian tahapan dalam penelitian ini meliputi pre-processing, augmentasi data, ekstraksi fitur, proses klasifikasi oleh kedua algoritma, dan dievaluasi menggunakan akurasi. Hasilnya, algoritma Support Vector Machine  dengan kernel Radial Basis Function mencapai 78% untuk algoritma K-Nearest Neighbor mencapai akurasi 80% dengan ketanggaan k=3
Deteksi Potensi Faktor Keberangkatan Jemaah Haji Menggunakan Algoritma Klasifikasi Machine Learning Farah Maulida, Oxana; Al Mudzakir, Tohirin; Yulia Novita, Hilda; Rohana, Tatang
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 9 (2025): JPTI - September 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.1060

Abstract

Haji merupakan salah satu rukun islam yang memiliki makna spiritual dan sosial mendalam bagi umat muslim diseluruh dunia Dengan meningkatnya jamaah haji di Indonesia setiap tahunnya, pengelolaan dan pelayanan terhadap calon jamaah haji menjadi tantangan. Faktor yang mempengaruhi seperti faktor demografis dari usia, pendidikan dan pekerjaan yang mempengaruhi keberangkatan jamaah. Penelitian ini bertujuan untuk mendeteksi faktor keberangkatan jamaah haji menggunakan algoritma machine learning, khususnya metode Naïve Bayes, Random Forest dan Decision Tree. Dataset yang dikumpulkan dari Kantor Kementerian Agama Karawang dan diolah menggunakan bahasa pemrograman Phyton. Proses penelitian meliputi pengumpulan data, preprocessing, split data, implementasi algoritma, dan evaluasi. Random Forest mencapai akurasi tertinggi sebesar 99.23%, Decision Tree mencatat akurasi 98.75%, dan Naïve Bayes memiliki akurasi 76.69%. Hasil evaluasi menunjukkan model mampu memberikan akurasi signifikan dalam mengidentifikasi kategori jamaah haji. Diharapkan penelitian ini akan memeberikan wawasan mendalam tentang klasifikasi data jamaah haji dan membantu instansi dalam perencanaan sumber daya yang kebih baik sehingga instansi dapat mengoptimalkan penggunaan anggaran dan alokasi sumber daya yang lebih efisien.
Optimasi Algoritma Machine Learning Menggunakan Seleksi Fitur Xgboost Untuk Klasifikasi Kanker Payudara Ramadhan, Naufal Cahya; H, Hanny Hikmayanti; Rohana, Tatang; Siregar, Amril Mutoi
TIN: Terapan Informatika Nusantara Vol 5 No 2 (2024): July 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v5i2.5408

Abstract

This research analyzes the performance of the K-Nearest Neighbors (KNN), Naïve Bayes, and Random Forest algorithms in the classification of breast cancer diagnosis using the Wisconsin Breast Cancer dataset. The problem discussed is how to improve the accuracy of breast cancer diagnosis classification through appropriate preprocessing techniques. The research objective is to evaluate and compare the performance of the three algorithms after the application of preprocessing which includes data cleaning, handling missing values, data duplication, and outliers, as well as feature selection using XGBoost and SMOTE oversampling. application of feature selection to identify the most relevant features and SMOTE to balance the class distribution in the dataset. Performance evaluation results using a confusion matrix show that Random Forest has the best performance with high accuracy, precision, recall, and F1-score, reaching an AUC of 98% after the application of SMOTE. The combination of feature selection and SMOTE was shown to significantly improve model performance, although KNN showed a decrease in performance with SMOTE, while Naïve Bayes experienced a considerable improvement. This study demonstrates the importance of preprocessing techniques in the development of machine learning models for medical applications, emphasizing that appropriate techniques can significantly improve classification performance and result in more accurate diagnoses.
CLASSIFICATION OF RICE ELIGIBILITY BASED ON INTACT AND NON-INTACT RICE SHAPES USING YOLO V8-BASED CNN ALGORITHM Hastari, Nazwa Putri; Rohana, Tatang; Masruriyah, Anis Fitri Nur; Wahiddin, Deden
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 5 (2024): JUTIF Volume 5, Number 5, Oktober 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.5.2413

Abstract

The large amount of unfit rice has an impact on the quality of rice provided to the community. This is due to the lack of supervision of the quality of existing rice, so that the quality of rice distributed to the community has a lot of unfit quality. Rice production for public consumption reached 21.69 million tons in 2021, according to data from the Central Statistics Agency (BPS). Rice is the main food of the Indonesian people because most Indonesians are farmers and the vast amount of agricultural land makes Indonesia one of the largest rice producing countries in Southeast Asia, this has a huge impact on people's habits in consuming rice as the main food provider. The Government of the Republic of Indonesia started a Social Assistance rice distribution program through the Ministry of Social Affairs in 2018. This program is named Prosperous Rice Social Assistance (Bansos Rastra). Classification of rice eligibility can be the first step to ensure that the rice received from the government is of high quality and can meet the daily needs of households in Indonesia. CNN algorithm based on YOLOv8 system can automatically recognize the form of rice given by the government whether it is feasible or not. In the research stages there are dataset collection, preprocessing, training models to evaluation. Based on the results obtained in this study, the accuracy achieved is 79% for the Eligible class and 79% for the Ineligible class with Confidence score reaching a value of 1.00. The results of this study can be used as a decent and unfit rice classification detection model by looking at the shape of the rice. So that the rice distributed to the community has decent rice quality.
Co-Authors Abigael, Rakha Adi Susilo Adinata, Abdul Rohim Agusti, Anggi Renata Agustin, Rachmayanti Tri Ahmad Fauzi Antoni, Alan Awal, Elsa Elvira Azhari, Febrian Akbar Azzahra, Fathimah Noer Baihaqi, Kiki Ahmad Bayu Prasetyo, Bayu Bunga Tiara, Vira Butar Butar, Naomi Nova Meylica Damaiarta Tejayanda, Rigger Faisal, Muhamad Agus Faisal, Sultan Faisal, Sutan Farah Maulida, Oxana Fauzi Ahmad Muda Fauzi, Farras Ahmad Fauzi, Rifqi Arul Fitri Nur Masruriyah, Anis Hakim, Mirwan Abdurrahman Hanny Hikmayanti Handayani Hartini, Dina Hastari, Nazwa Putri Indra, Jamaludin Irawan, Muhamad Anggi Jamal, Malikil Juwita, Ayu Ratna Kamilah, Nur Azizah Karyanto, Dony Dwi Kusumaningrum, Dwi Sulistya Kusumaningrum, Dwi Sulistya Kusumaningrum Laurentzia, Rini Beatrix Lestari, Santi Arum Puspita Madani, Puja Milenia Sriwildan Maulana, Moch Sigit Rizky Mudzakir, Tohirin Al Muhammad Khaerudin Munzi, Gugy Guztaman Niendha Biell Binna Novita, Hilda Yulia Nur ‘Azah Nurlaelasari, Euis Nurlaila, Diah Nurmayanti, Trisya Nursyawalni, Reva NURUL AZIZAH Pratama, Adi Rizky Pratama, Irfan Nugraha Purnomo, Indarto Aditya Rahmat Rahmat Ramadhan, Muchamad Fachrul Ramadhan, Naufal Cahya Ramadhan, Rizky Agung Ratna Juwita, Ayu Romadoni, Nurul Siregar, Amril Mutoi Sukmawati, Cici Emilia Syahril, Ade Tri Vicika, Vikha Wahiddin, Deden Winarni Yana Cahyana Yulia Novita, Hilda