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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) J-SAKTI (Jurnal Sains Komputer dan Informatika) 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 Jurnal Media Informatika 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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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.
CLASSIFICATION OF FAMILY HOPE PROGRAM RECIPIENTS USING NAIVE BAYES AND C4.5 METHODS Fauzi, Farras Ahmad; Rohana, Tatang; Juwita, Ayu Ratna; 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.2024.5.5.3697

Abstract

Receiving PKH assistance in Rawamerta District does not always go well, so there are people who are not entitled to receive assistance. This is because there is still no system that can facilitate the process of classifying PKH assistance recipients. The application of data mining can facilitate classification with high speed and accuracy. The purpose of this study is to classify PKH assistance recipients using the Naïve Bayes and C4.5 methods to determine the eligibility of PKH for people facing social welfare problems. The data used is PKH data in Rawamerta District, Karawang Regency in 2023, totaling 1834 data. The results of naive bayes accuracy of 98.89%, precision 98.25%, recall 98.51%, F1-score 98.89%, and AUC 1.00 are included in the excellent classification because they are in the range of 0.90-1.00, while the C4.5 algorithm produces Accuracy values ​​of 99.26%, Precision 99.25%, Recall 99.25%, F1-score 99.25% and AUC 0.99 are included in the excellent classification because they are in the range of 0.90-1.00. The C4.5 algorithm is superior to Naive Bayes, because the accuracy produced is higher.
Classification Model of Public Sentiments About Electric Cars Using Machine Learning Romadoni, Nurul; Siregar, Amril Mutoi; Kusumaningrum, Dwi Sulistya; Rohana, Tatang
Scientific Journal of Informatics Vol. 11 No. 2: May 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i2.1309

Abstract

Purpose: This research compared the accuracy level of six algorithms based on the ROC method and the Confusion Matrix evaluation on data regarding public sentiments towards electric cars. Methods: Data collection was conducted for data sourced from TikTok. Next, the data underwent text preprocessing (data cleaning and case folding) and text processing (stemming, tokenizing, stopword removal, word frequency, word relation, TF-IDF, scoring, and labeling). Modeling was then conducted using supervised (labeled) algorithms consisting of the Support Vector Machine (SVM), Decision Tree, Naive Bayes, Random Forest, K-Neighbor, and Logistic Regression. Finally, an evaluation was conducted (confusion matrix and ROC). Result: The results revealed that the Decision Tree algorithm with the Confusion Matrix and ROC evaluation obtained the highest result of 87%. The algorithm with the lowest result is KNN, which has an accuracy of 56%. The classification result for the neutral sentiment has a percentage of 57.1%, followed by negative sentiment at 26.8% and positive sentiment at 16.1%. The KNN algorithm is suitable for large and low-dimensional data, SVM is suitable for data with many features and clear separation between classes, and Naive Bayes is efficient for large datasets with many low-quality features. Additionally, the Random Forest algorithm could overcome overfitting and unbalanced data. Logistic regression is also suitable for linear data without assuming a certain distribution. The Decision Tree algorithm is good for complex data as it provides a visual explanation of predictions. In this study, the Decision Tree algorithm obtained high results because it has the best characteristics and is a linear technique. Novelty: This study found that based on the ROC method and the Confusion Matrix evaluation conducted, the Decision Tree algorithm is more accurate than the other algorithms studied.
Sistem Presensi Praktikum Berbasis Web Menggunakan Algoritma Brute Force Awal, Elsa Elvira; Rohana, Tatang; Munzi, Gugy Guztaman; Nurlaelasari, Euis; Tri Vicika, Vikha; Nurlaila, Diah; Laurentzia, Rini Beatrix
Innovative: Journal Of Social Science Research Vol. 4 No. 1 (2024): Innovative: Journal Of Social Science Research
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/innovative.v4i1.9160

Abstract

Penelitian ini mengusulkan dan menerapkan sistem presensi praktikum menggunakan algoritma Brute Force dengan teknologi QR Code. Algoritma Brute Force digunakan untuk menentukan kehadiran mahasiswa dengan memeriksa lokasi geografis mereka terhadap empat garis batas yang dibuat berdasarkan koordinat geografis. Penggunaan QR Code sebagai metode pemindaian memfasilitasi proses presensi dengan efisiensi dan akurasi yang tinggi. Penelitian ini melibatkan pengembangan antarmuka pengguna untuk mahasiswa dan administrator, serta evaluasi kinerja algoritma dalam skenario praktikum yang berbeda. Hasil penelitian menunjukkan bahwa algoritma Brute Force memberikan solusi yang akurat dan dapat diandalkan, dengan potensi untuk diintegrasikan dalam sistem presensi di lingkungan praktikum universitas. Saran untuk pembaruan dan optimalisasi kontinu diberikan untuk meningkatkan kinerja dan keberlanjutan sistem presensi. Penelitian ini berkontribusi pada pemahaman tentang potensi implementasi algoritma Brute Force dalam mengoptimalkan presensi mahasiswa dalam praktikum.
Penerapan Metode Naive Bayes Dalam Klasifikasi Spam SMS Menggunakan Fitur Teks Untuk Mengatasi Ancaman Pada Pengguna Azzahra, Fathimah Noer; Rohana, Tatang; Rahmat, Rahmat; Juwita, Ayu Ratna
Journal of Information System Research (JOSH) Vol 5 No 3 (2024): April 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v5i3.5070

Abstract

One of the negative impacts of current digital advances is the increasing number of SMS spam. Spam SMS poses a security risk to users because they can contain malicious links or requests for personal information that are used for malware, smishing, or fraud attacks. However, with the various protection measures available, not all spam SMS can be classified and prevented effectively. However, this problem can be minimized by creating an anti-spam SMS model which aims to classify SMS types. So this research aims to classify types of SMS that contain spam and spam by applying the Naïve Bayes algorithm. In this study, the dataset consisted of 5572 records consisting of 2 categories, namely spam and ham. This algorithm is able to show satisfactory performance in differentiating spam and spam messages because, according to the diversity of literature, the Naïve Bayes algorithm is suitable for use in English language datasets. The evaluation model displays good results with accuracy reaching 93.2%, precision 93.7%, recall 93.2%, and F1-score 91.6%. In addition, analysis in the research using the Receiver Operating Characteristic (ROC) curve shows an accuracy rate of 97.3%, indicating that the model has very good performance in classifying spam in SMS messages. However, there is still room for improvement through the use of new methods and larger and more diverse data sets. This research has an important involvement in working on communication security and user experience in using short message services.
Perbandingan Metode Decision Tree Dan K-Nearest Neighbor Terhadap Ulasan Pengguna Aplikasi Mypertamina Menggunakan Confusion Matrix Syahril, Ade; Cahyana, Yana; Kusumaningrum, Dwi Sulistya; Rohana, Tatang
Journal of Information System Research (JOSH) Vol 5 No 4 (2024): Juli 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v5i4.5639

Abstract

The large number of vehicles in Indonesia makes fuel oil (BBM) very important, especially for cars and motorbikes. The Indonesian government works closely with PT Pertamina Persero and requires transactions using the MyPertamina application to ensure that fuel subsidies are properly targeted. However, the MyPertamina app has received mixed feedback and criticism from users, such as complaints about frequent bugs, instability of the app during use and difficulties in the registration or login process. User feedback on the app has been both positive and negative. Users also provided their ratings and reviews on the Google Play Store. The purpose of this research is to analyse the opinions of MyPertamina application user comments and compare the accuracy of the Decision Tree and K-Nearest Neighbor algorithms. This research includes scraping, text preprocessing, weighting, algorithm implementation and evaluation. The data used was obtained from Google Play Store as much as 10,000 data based on the latest reviews, after data cleaning such as removing duplicate data and missing values obtained 8,072 reviews. The data is then grouped into positive classes (2,506 reviews) and negative classes (5,566 reviews), with more negative data. The classification results using the Decision Tree and K-NN methods, it is known that the Decision Tree method has a higher accuracy of 83%, while K-NN method is 58%. This finding indicates that the Decision Tree method is more effective in analysing user reviews of the MyPertamina application compared to the K-NN method.
Identifikasi Penyakit Diabetes Mellitus Menggunakan Algoritma Support Vector Machine dan Random Forest Agusti, Anggi Renata; Fauzi, Ahmad; Baihaqi, Kiki Ahmad; Rohana, Tatang
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 4 (2025): Agustus 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i4.8686

Abstract

Diabetes mellitus is a chronic metabolic disease that is increasingly common in Indonesia, estimated to affect more than 10.8 million people in 2020. This disease needs to be recognized early to prevent serious complications that can increase morbidity and mortality. By comparing the two methods, this study was conducted to determine whether one approach shows a better level of accuracy and to develop a classification model based on patient data. The research data was provided by the Anggadita Health Center which includes demographic data, lifestyle, and health assessment results from 1001 patients. One of the research steps is data pre-processing to evaluation. SVM and RF modeling can evaluate models using accuracy, precision, recall, and F1-score metrics. Based on the test results, the Random Forest algorithm showed the best performance with an accuracy of 99%, precision of 99%, recall of 100%, and F1-score of 99%, while SVM got an accuracy of 91%, precision of 0.93%, recall of 0.91%, and F1-score of 0.92%. This shows how well Random Forest separates patients with and without diabetes. This study is expected to be one of the references in obtaining information for making medical decision support systems so that health workers can be faster and more accurate in diagnosing diabetes mellitus.
DETECTION OF THE SIZE OF PLASTIC MINERAL WATER BOTTLE WASTE USING THE YOLOV5 METHOD Karyanto, Dony Dwi; Indra, Jamaludin; Pratama, Adi Rizky; Rohana, Tatang
JIKO (Jurnal Informatika dan Komputer) Vol 7, No 2 (2024)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i2.8535

Abstract

The use of plastic bottles for various needs is increasingly massive, especially in consumption needs such as mineral water bottles. The use of plastic bottles is used to reduce costs and be effective in maintaining the quality of mineral water, but its impact can affect natural conditions if not managed properly. Plastic bottle waste if left buried in the ground will have difficulty expanding, which can cause environmental pollution. Therefore, we can take advantage of technology to sort plastic bottle waste using a camera based on the size of plastic bottles. Differentiating the size of bottles aims to distinguish the economic value when exchanged at the waste bank. This technology utilizes object detection and recognition functions such as the YOLO (You Only Look Once) method. YOLO is a detection method that is a development of the CNN (Convolutional Neural Network) algorithm. By using YOLOv5, we can detect objects in the form of plastic bottle waste of various different sizes. To maximize object detection according to size, data annotation is done by creating a Bounding Box on each dataset according to its size. The test was carried out with several different distance configurations including 40cm, 80cm and 1m. Detection results using YOLOv5 produce up to 84% accuracy in real-time.
Pemodelan Prediksi Ekspor Kopi Indonesia Berbasis Algoritma Machine learning Novita, Hilda Yulia; Rohana, Tatang; Nurlaelasari, Euis; Awal, Elsa Elvira
Jurnal Media Informatika Vol. 6 No. 6 (2025): Edisi Desember 2025
Publisher : Lembaga Dongan Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55338/jumin.v6i6.7097

Abstract

Penelitian ini bertujuan untuk membangun model prediksi ekspor kopi di Indonesia dengan menggunakan tiga algoritma machine learning, yaitu regresi inier, neural networks, dan gradient boosting. Data yang digunakan berasal dari data historis ekspor kopi Indonesia. Penelitian dilakukan melalui tahapan pra-pemrosesan data, pemodelan, dan evaluasi kinerja masing-masing algoritma. Hasil penelitian menunjukkan bahwa ketiga algoritma mampu memprediksi ekspor kopi dengan performa yang cukup baik. Algoritma Linear Regression memberikan hasil terbaik dengan nilai mean squared error (MSE) sebesar 0.0000867, mean absolute error (MAE) sebesar 0.00766, dan skor R² sebesar 95%. neural networks menghasilkan MSE sebesar 0.000171, MAE sebesar 0.01196, dan skor R² sebesar 91%. Sementara itu, gradient boosting menunjukkan performa terendah dengan MSE sebesar 0.01918 dan skor R² sebesar 74%. Temuan ini menunjukkan bahwa pendekatan machine learning dapat digunakan sebagai alat bantu dalam memprediksi tren ekspor komoditas secara akurat.
Co-Authors Abigael, Rakha Adi Susilo Adinata, Abdul Rohim Agusti, Anggi Renata Agustin, Rachmayanti Tri 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, Ahmad 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