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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Robotics and Automation (IJRA) IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics Jurnal Informatika Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Journal of ICT Research and Applications JUITA : Jurnal Informatika MUSTEK ANIM HA Scientific Journal of Informatics JOIV : International Journal on Informatics Visualization Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) SISFOTENIKA Wikrama Parahita : Jurnal Pengabdian Masyarakat IT JOURNAL RESEARCH AND DEVELOPMENT JURNAL REKAYASA TEKNOLOGI INFORMASI SINTECH (Science and Information Technology) Journal JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi MIND (Multimedia Artificial Intelligent Networking Database) Journal KOMPUTIKA - Jurnal Sistem Komputer TELKA - Telekomunikasi, Elektronika, Komputasi dan Kontrol Building of Informatics, Technology and Science JISKa (Jurnal Informatika Sunan Kalijaga) Jurnal Informatika dan Rekayasa Elektronik Journal of Innovation Information Technology and Application (JINITA) Infotek : Jurnal Informatika dan Teknologi SKANIKA: Sistem Komputer dan Teknik Informatika Innovation in Research of Informatics (INNOVATICS) Jurnal Teknik Informatika (JUTIF) Jurnal PTI (Jurnal Pendidikan Teknologi Informasi) Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer) JUSTIN (Jurnal Sistem dan Teknologi Informasi) Transformasi PROSISKO : Jurnal Pengembangan Riset dan observasi Rekayasa Sistem Komputer JOMPA ABDI: Jurnal Pengabdian Masyarakat Jurnal Pengabdian Masyarakat Intimas (Jurnal INTIMAS): Inovasi Teknologi Informasi Dan Komputer Untuk Masyarakat Data Sciences Indonesia (DSI) Journal Of Artificial Intelligence And Software Engineering Jurnal INFOTEL Journal of Computer Science and Information Technology Inovasi Teknologi Masyarakat Jurnal Pengabdian Siliwangi
Anindita Septiarini, Anindita
Mulawarman University
Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Physics Social Sciences Other

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A comparative study of machine learning methods for drug type classification Tejawati, Andi; Suprihanto, Didit; Ery Burhandenny, Aji; Saipul, Saipul; Puspitasari, Novianti; Septiarini, Anindita
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.9477

Abstract

Drugs, commonly called narcotics, are dangerous substances that, if consumed excessively, can result in addiction and even death. Drug abuse in Indonesia has reached a concerning stage. In 2017, the National Narcotics Agency detected 46,537 drug-related incidents, including methamphetamine, marijuana, and ecstasy. There are 4 types of substances that can affect drug users, such as hallucinogens, depressants, opioids, and stimulants. A machine learning approach can detect these substances using user symptom data as input. This study uses six different methods in classifying, including decision tree, C.45, K-nearest neighbor (KNN), random forest, and support vector machine (SVM). The dataset comprises 144 data and 21 attributes based on the user's symptoms. The evaluation method in this study uses cross-validation with K-fold values of 5 and 10 and uses three parameters: precision, recall, and accuracy. KNN yields the most optimal results by using K=1 and K-fold 10 in the Euclidean and Minkowski types. The model achieves precision, recall, and accuracy of 91.9%, 91.7%, and 91.67%, respectively.
Implementasi Arsitektur Recurrent Neural Network Pada Analisis Sentimen Clash of Champions Arif Hidayat; Anindita Septiarini; Medi Taruk
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 8 No 2 (2025): Jurnal SKANIKA Juli 2025
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v8i2.3586

Abstract

Clash of Champions is an educational program by Ruangguru on YouTube that has received mixed responses. This study aims to perform sentiment analysis using three Recurrent Neural Network (RNN) architectures: Vanilla Recurrent Neural Network (Vanilla RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The data consists of 2,100 training samples, 300 validation samples, and 600 testing samples collected from YouTube and enriched with data augmentation using GPT-4 technology. Additionally, 35 comments from a survey conducted via Google Form are used for generalization testing. Comments are classified into three sentiments: Pro, Neutral, and Contra. The analysis involves preprocessing, model training, and evaluation using standard metrics. GRU demonstrated the best performance with an accuracy of 99.2% and the highest F1 score. LSTM achieved an accuracy of 99.0% and a recall of 100% for the Pro class, while Vanilla RNN was less stable. On real-world data, GRU correctly predicted 16 comments, outperforming LSTM with 14 correct predictions and RNN with 13 correct predictions. GRU excels in accuracy, stability, and adaptability to the data.
Enhancing crude palm oil quality detection using machine learning techniques Puspitasari, Novianti; Hairah, Ummul; Kamila, Vina Zahrotun; Hamdani, Hamdani; Septiarini, Anindita; Masa, Amin Padmo Azam
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp2955-2963

Abstract

Indonesia, a leading nation in the palm oil industry, experienced a significant increase of 15.62% in crude palm oil (CPO) exports in 2020, effectively meeting the global need for vegetable oil and fat. Therefore, the subjective assessment of CPO quality, influenced by differences in human evaluations, may lead to inconsistencies, necessitating the adoption of machine learning methods. There are several categories of CPO, such as bad and excellent. Machine learning can determine the quality of CPO itself. This study utilizes two distinct categories to measure the quality of CPO. CPO quality data is collected and processed into pre-processing data, in classifying using several methods such as artificial neural network (ANN), k-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), naïve Bayes (NB), and C.45 using the cross-validation evaluation parameter. The best results are obtained by C.45 and DT with an accuracy of 99.98%.
IMPLEMENTASI LOGIKA FUZZY MAMDANI DALAM SISTEM PENILAIAN KESEHATAN MAKANAN KEMASAN BERDASARKAN LABEL NUTRITION FACTS Ahmad Nur Fauzan; Muhammad Abdillah; Reviansa Fakhruddin Aththar; Anindita Septiarini; Masna Wati
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 11 No. 2 (2025): Volume 11 Nomor 2 Tahun 2025
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/mtk.v11i2.4334

Abstract

The growth of the packaged food industry has increased the need for an easy-to-understand health assessment system for consumers, especially those with limited nutrition literacy. This study develops a Mamdani fuzzy logic-based decision support system to evaluate the healthiness of packaged foods using Nutrition Facts labels. The system processes nutritional parameters such as fat, sugar, salt, fiber, protein, fruit/vegetable/nut content, and calorie content, converting them into linguistic categories like "low," "moderate," and "high" for easier interpretation by lay users. It effectively handles uncertainties and ambiguities in nutrition data, providing classifications like "Unhealthy," "Healthy," or "Very Healthy." Implemented through a web platform using Python and Flask, the system was tested with five food samples, achieving an 80% agreement with the official NutriScore classification. This indicates the potential of the system as a reliable, practical tool to help consumers make quicker and more accurate dietary decisions and improve nutrition awareness.
Implementasi XGBoost dalam Klasifikasi Gagal Ginjal Kronis Menggunakan Dataset Chronic Kidney Disease Abdillah, Muhammad; Sarira, Brayen Tisra; Hidayat, Ahmad Nur; Fauzan, Ahmad Nur; Nurhidayat, Rifki; Septiarini, Anindita; Puspitasari, Novianti
JATISI Vol 12 No 3 (2025): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v12i3.11546

Abstract

Chronic Kidney Disease (CKD) is a serious health issue that can lead to death if not detected early. To support early detection, this study applies the eXtreme Gradient Boosting (XGBoost) algorithm to classify patients at risk of CKD. The dataset used is the Chronic Kidney Disease Dataset from Kaggle, consisting of 400 patient records and 26 clinical attributes. Preprocessing involved imputing missing values and converting categorical features into numerical form. The model was evaluated using accuracy, precision, recall, and F1-score metrics. The results show that XGBoost achieved 99% accuracy, with 98% precision and 100% recall, indicating excellent performance in binary classification tasks. This study demonstrates that XGBoost is a reliable algorithm for automatic prediction of chronic kidney disease. Keywords: XGBoost, chronic kidney disease, classification, machine learning
Comparison of YOLOv5 for Classifying Mangrove Leaf Species using CNN-Based Septiarini, Anindita; Diana, Rita; Kamara, Rahmat; Puspitasari, Novianti; Prafanto, Anton
Journal of Innovation Information Technology and Application (JINITA) Vol 7 No 1 (2025): JINITA, June 2025
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v7i1.2676

Abstract

Indonesia has many species of mangrove plants scattered throughout the coast to the river's edge. Species of mangrove plants can be distinguished based on root type, stem size, leaf shape, flower color, and fruit. Although each type of mangrove plant has different characteristics, several types look similar, especially on the leaves. Therefore, a model was needed to classify mangrove plant species by applying current technology to make it easier to recognize the type of mangrove plant. This research aims to implement the Convolutional Neural Network (CNN) method in classifying mangrove plant species. The algorithm used is the 5th version of You Only Look Once (YOLO) with 3 different variants (YOLOv5s, YOLOv5m, and YOLOv5l). The three variants have various processing times and numbers of layers. This study uses mangrove leaf images with a total image dataset of 400 images consisting of 4 types of mangrove plants: Avicennia alba, Bruguiera gymnorhiza, Rhizopora apiculata, and Sonneratia alba. The model performance achieved 82.50%, 88.75%, and 93.75% accuracy using YOLOv5s, YOLOv5m, and YOLOv5l, respectively.
Enhanced Semarang batik classification using deep learning: a comparative study of CNN architectures Winarno, Edy; Solichan, Achmad; Putra Ramdani, Aditya; Hadikurniawati, Wiwien; Septiarini, Anindita; Hamdani, Hamdani
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9347

Abstract

Batik is an important part of Indonesia’s cultural heritage, with each region producing unique designs. In Central Java, Semarang is known for its distinctive batik patterns that reflect rich local traditions. However, many people are still unfamiliar with these designs, which threatens their preservation. This study develops an automated system to classify Semarang batik patterns, showing how technology can help safeguard cultural heritage. A convolutional neural network (CNN) approach was used to recognize ten batik types, including Asem Arang, Asem Sinom, Asem Warak, Blekok, Blekok Warak, Gambang Semarangan, and Kembang Sepatu. Pre-processing steps—such as image resizing, cropping, flipping, and rotation—improved model performance and reduced complexity. Five CNN architectures (MobileNetV2, ResNet-50, DenseNet-121, VGG-16, and EfficientNetB4) were tested using 224×224 input size, Adam optimizer, ReLU activation, and categorical cross-entropy loss. Results show VGG-16, ResNet-50, and DenseNet-121 achieved perfect accuracy (1.0) on a dataset of 3,000 locally collected images. These findings highlight CNN models’ strong potential for batik pattern recognition, supporting digital preservation of Indonesian culture.
Penerapan Metode K-Means Clustering Status Gizi Balita Di UPT Puskesmas Barong Tongkok Vicky Pranandika Wijaksana; Hairah, Ummul; Wati, Masna; Puspitasari, Novianti; Septiarini, Anindita
Data Sciences Indonesia (DSI) Vol. 5 No. 1 (2025): Article Research Volume 5 Issue 1, June 2025
Publisher : Yayasan Cita Cendikiawan Al Kharizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/dsi.v5i1.6517

Abstract

Gizi pada anak balita merupakan masalah yang sangat penting untuk diperhatikan terutama bagi orang tua dan tenaga kesehatan. Status gizi balita dapat diketahui berdasarkan indeks Berat Badan menurut Umur dan Tinggi Badan menurut Umur. Penelitian ini bertujuan untuk mengidentifikasi pola-pola yang mungkin ada dalam status gizi balita dan mengidentifikasi kelompok balita yang berisiko tinggi atau berada dalam kondisi gizi yang buruk pada balita di kecamatan Barong Tongkok dengan penerapan K-Means. Data yang digunakan sebanyak 300 data yang akan dicluster menjadi 3 yaitu Underweight, Gizi Baik dan Gizi Lebih menggunakan metode perhitungan jarak Ecludean Distance, Manhattan Distance dan Minkowski Distance. Hasil pengujian Sum Squared Error (SSE) menunjukkan metode Minkowski Distance lebih unggul karena mendapatkan nilai error terkecil sebesar 815,4409. Sebanyak 133 Balita dalam kategori Gizi Baik (C1), 83 Balita dalam kategori Gizi Lebih (C2), dan 84 Balita dalam kategori Underweight (C3).
Autoregressive Integrated Moving Average (ARIMA) Model for Forecasting Indonesian Crude Oil Price Wati, Masna; Haviluddin, Haviluddin; Masyudi, Akhmad; Septiarini, Anindita; Hatta, Heliza Rahmania
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 3 (2023): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i3.22286

Abstract

Crude oil is the main commodity of the global economy because oil is used as an ingredient for many industries globally and is the price base used in the state budget. Indonesian Crude Price (ICP) fluctuates following developments in world crude oil prices. A significant increase in crude oil prices will certainly disrupt the economy. Thus, the movement or fluctuation of ICP is essential for business players in the energy market, especially domestically. Therefore, crude oil price forecasting is needed to assist business people in making decisions related to the energy market. This study aims to find a suitable forecasting model for Indonesian crude oil prices using the Autoregressive Integrated Moving Average (ARIMA) method. The forecasting process used ICP time-series data per month for 50 types of crude oil within five years or 63 months. Based on the experimental results, it was found that the most fit ARIMA models were (0,1,1), (1,1,0), (0,1,0), and (1,2,1). The test results for April to September 2020 have a good and proper interpretation, except the type of BRC oil indicates inaccurate forecasts. The ARIMA error rate is very dependent on the value of the data before it is predicted and external factors, the more unstable the data value every month, the higher the error rate.
Implementasi Logika Fuzzy Mamdani Dalam Klasifikasi Kategori Berat Badan Berbasis IMT Ambon, Matelda Yunanta; Lili, Juniver Veronika; Bandhaso, Victor; Wati, Masna; Septiarini, Anindita
Infotek: Jurnal Informatika dan Teknologi Vol. 8 No. 2 (2025): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v8i2.30637

Abstract

Body Mass Index (BMI) is a common method used to classify body weight based on the ratio of weight to height. However, its accuracy is often questioned because it does not account for age and gender, which also influence body composition. This study implements the Mamdani fuzzy logic approach to classify body weight based on BMI while considering age and gender. The system utilizes fuzzy membership functions to dynamically determine categories such as Underweight, Normal, Overweight, and Obese, and is developed using the Python programming language with interactive visualizations. Testing results show that the system can provide more adaptive and personalized classifications. Defuzzification values, such as 59.48 for a BMI of 24.22, indicate a classification consistent with WHO standards—namely, the Normal category. The system also demonstrates that classification results may vary for the same BMI when age or gender differs, as illustrated in multi-demographic visualizations. The centroid defuzzification method produces stable and representative outputs. Evaluation results show high accuracy, consistency in rule base, and an ability to handle data uncertainty. Thus, this system serves as a more flexible alternative to conventional methods in body weight classification.
Co-Authors Abdul Razak Aliudin Adi Muhammad Syifai Adnan, Fahrizal Afifah, Dinda Nur Agus Qomaruddin Munir AHMAD ANSYORI Ahmad Nur Fauzan Ajay, Muhammad Akhmad Masyudi Alameka, Faza Alif Rifa’i Alvito Gabbriel Saputra Ambari, Nasser Ambon, Matelda Yunanta Andri Syafrianto Anggari, Ricky Annisa Putri Novalianti Anton Prafanto ARIF HIDAYAT Arini Wijayanti Asmita, Rizka Aulia Rahman Awang Harsa Kridalaksana Awang Zheri Rhesvianur Az Zahrah, Rezha Nur Bandhaso, Victor Briyan Efflin Syahputra Budi Rahmani Budiman, Edy Cakra Dewandaru Christy Maulidiah Daffa Putra Mahardika Diana, Rita Didit Suprihanto, Didit Dwi Prasetio Dyna Marisa Khairina Edy Winarno Eka Priyatna, Surya Enny Itje Sela Ery Burhandenny, Aji Ery Burhandeny, Aji Evi Wildana Fahrozi, Muhammad Naufal Fairil Anwar Fajri, Muhamad Mushfa Hikmatal Fandi Alief Al Akbar Fathia Nuq Qamarina Fauzan, Ahmad Nur Fayza Virdana Addiza Firyal, Tasya Nadina Fornia, Daviana Dwitasari Enka Fuad, Natalie Gempar Panggih Dwi Gunawan, Ayu Lestari Hairah, Ummul Hairah, Ummul Hakim, Muhammad Irvan Hamdani Hamdani . Hamdani Hamdani Hamdani Hamdani Hamdani Hamdani Hamdani Hamdani Hanif, Ahmad Luthfi Hatta, Heliza Rahmania Haviluddin Haviluddin Haviuddin, Haviluddin Heliza Hatta Heliza Rahmania Hatta, Heliza Rahmania Henderi . Heni Sulastri Heru Ismanto Hidayat, Ahmad Nur Hutagalung, Wilson Boyaron Hutapea, Vedra Dian Sierrafina Ibnu Amri Thaher Ifnu Umar Indah Fitri Astuti Indah Wulan Lestari Irfan, Aliya Kalingga Dwindra Putraka Kamara, Rahmat Kamila, Vina Zahrotun Kiki Purwanti Laraswati, Sherina Lempas, Gidion Lili, Juniver Veronika M. Rizky Nilzamyahya Maharani, Agustina Dwi Mahendra, Dicky Alvian Masa, Amin Padmo Azam Masna Wati Masyudi, Akhmad Medi Taruk Mewengkang, Alfrina Muhamad Azhari Muhammad Abdillah Muhammad Abdillah Muhammad Andas Lesmana Muhammad Dzacky Muhammad Ifandi Muhammad Nur Ramadhan Muhammad Sofian Sauri Mu’nisah Assisi Nanda Arianto Nathaniela Aptanta Parama Nggotu, Antonieta Aryuka Paskalia Novianti Puspitasari Nupa, Joy Disanto Nur Madia Nurcahyono, Damar Nurhidayat, Rifki Nurmadewi, Dita Olivia Octavia Padmo Azam Masa, Amin Patricia Chandra Pebianoor, Pebianoor Prafanto, Anton Pramudya, Pranata Eka Pratiwi, Sinthya Ayu Puspitasari, Novianti Puspitasari, Novitanti Putra Ramdani, Aditya Putri, Septi Aulia Rafi Ichsanul Iqbal Raihanfitri Adi Kalipaksi Ramadhaniaty, Dinda Reski Harisma Dewi Barkah Reviansa Fakhruddin Aththar Risky Kurniawan Riswandi Syam Riyayatsyah, Riyayatsyah Rizqi Saputra Rondongalo Rismawati Rosmasari, Rosmasari Sadewa, Bintang Putra Saipul, Saipul Sakti, Dwi Nika Salsabila, Nur Maya Saragih, Muhammad Nabil Sarira, Brayen Tisra Satria Bagus Eka Chandra Saucha Diwandari Setiawan, Maulana Agus Sihombing, Yobel Fernanda Siti Retno Wulandari Sugandi Sugandi Sumaini Sumaini Supriyono Supriyono Supriyono Supriyono Syaffira Rizky Amalia Taruk, Medi Tejawati, Andi Tulili, Hadie Pratama Ummul Hairah Vicky Pranandika Wijaksana Viny Christanti M Wahyudi, Moh Ikhwan Wati, Masna Wibisono, Bramantyo Ardi Harimurti Widians, Joan Angelina Wintin, Chintia Liu Wiwien Hadikurniawati Yanuar Satria Gotama Yasmin, Annisa Yudi Sukmono, Yudi Yuyun Nabilawati Rumbia zahra salsabila Zainal Arifin