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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 Jurnal Teknologi Informatika dan Komputer 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 Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) 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) Jurnal Masyarakat Madani Indonesia 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 Public Health Social Sciences Other

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Journal : IAES International Journal of Artificial Intelligence (IJ-AI)

 1 
Dengue classification method using support vector machines and cross-validation techniques Hamdani Hamdani; Heliza Rahmania Hatta; Novianti Puspitasari; Anindita Septiarini; Henderi Henderi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 3: September 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i3.pp1119-1129

Abstract

Dengue is a dangerous disease that can lead to death if the diagnosis and treatment are inappropriate. The common symptoms that occur, including headache, muscle aches, fever, and rash. Dengue is a disease that causes endemics in several countries in South Asia and Southeast Asia. There are three varieties of dengue, such as dengue fever (DF), dengue hemorrhagic fever (DHF), and dengue shock syndrome (DSS). This disease can currently be classified using a machine learning approach with the input data being the dengue symptoms. This study aims to classify dengue types consisting of three classes: DF, DHF, and DSS using five classification methods including C.45, decision tree (DT), k-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM). The dataset used consists of 21 attributes, which are the dengue symptoms. It was collected from 110 patients. The evaluation method was conducted using cross-validation with k-folds of 3, 5, and 10. The dengue classification method was evaluated using three parameters: precision, recall, and accuracy, which were most optimally achieved. The most optimal evaluation results were obtained using SVM with k-fold 3 and 10 with precision, recall, and accuracy values reaching 99.1%, 99.1%, and 99.1%, respectively.
Recommendation method for selecting the rice seeds based on group decision support system Hamdani, Hamdani; Wati, Masna; Suprihanto, Didit; Salsabila, Nur Maya; Septiarini, Anindita; Nurmadewi, Dita; Mawardi, Viny Christanti
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp2656-2665

Abstract

In this paper, we provide group recommendations based on each decision makers (DMs) in choosing the best type of rice for replanting. This group decision support system (GDSS) aims to guide stakeholders who have a role in selecting rice types. In this method, we propose using technique for order preference by similarity to ideal solution (TOPSIS) to rank each DM, Borda to rank in groups, and then test it using Spearman's rank correlation to measure the relationship between system results and the method applied. The results of this study show that DM1 ranks highest in selecting Gelagai rice seeds with a preference of 0.7786. Then DM2 ranked highest with Ekor Payau rice seeds in preference 0.6529. Meanwhile, DM3 ranked highest in Gelagai rice seeds with a selection of 0.7728. The final group voting system uses Borda, where Gelagai rice seeds occupy the highest rank with the most accumulated votes from each DMs. The best option or the highest rating based on the assessment of the three DMs, DM1 as a farmer is the first rank A10 Gelagai with a score of Borda 26 in the decision group selection of superior rice seeds.
Image analysis for classifying coffee bean quality using a multi-feature and machine learning approach Septiarini, Anindita; Hamdani, Hamdani; Ery Burhandeny, Aji; Nurcahyono, Damar; Eka Priyatna, Surya
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4241-4248

Abstract

Price and customer satisfaction depend on coffee bean quality. The coffee industry must analyze coffee bean quality. Global demand for robusta coffee is high. Coffee industry professionals mostly understand coffee bean quality. Thus, an image analysis using a computer vision-based approach for classifying robusta coffee bean quality is required. Image acquisition, region of interest (ROI) detection, pre-processing, segmentation, feature extraction, feature selection, and classification are covered in this study. A multi-feature derived based on color, shape, and texture features was employed in feature extraction, followed by feature selection using principal component analysis (PCA). Several machine-learning methods classified the coffee beans. The method performance was assessed using precision, recall, and accuracy. The selected features using the backpropagation neural network (BPNN) classifier outperformed others with 98.54% accuracy.
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%.
Automated ergonomic sitting postures detection for office workstation using XGBoost method Pawitra, Theresia Amelia; Sitania, Farida Djumiati; Septiarini, Anindita; Hamdani, Hamdani
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp506-514

Abstract

Sedentary office work increases musculoskeletal risk, underscoring the need for non-intrusive, real-time posture monitoring. This study presents a computer vision approach that classifies ergonomic versus non-ergonomic sitting postures using upper body key points extracted by MoveNet thunder. Images from 30 participants were captured from frontal and side views, and labeled according to SNI 9011:2021 criteria. Seventeen key points were detected, with head-to-hip landmarks retained, then normalized and centered. Three classifiers—adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), and a multi-layer perceptron (MLP)—were trained and evaluated with 10-fold stratified cross-validation. XGBoost achieved the best performance, with accuracy 93.0%±1.9%, precision 94.6%, recall 91.4%, F1-score 92.9%, and area under the receiver operating characteristic curve (ROC-AUC) 0.974±0.010, outperforming MLP and AdaBoost. The method supports privacy-preserving, on-device inference and is suitable for integration into smart office systems to reduce exposure to high-risk postures. Limitations include controlled capture conditions and an upper body focus; future work will expand posture taxonomy and real-world deployment.
Co-Authors Abdul Razak Aliudin Achmad Solichan 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 Ardi Setyiawan ARIF HIDAYAT Arini Wijayanti Asmita, Rizka Aulia Rahman Awang Harsa Kridalaksana Awang Zheri Rhesvianur Az Zahrah, Rezha Nur Azzahra, Raudhya Bandhaso, Victor Briyan Efflin Syahputra Budi Rahmani Budiman, Edy Cakra Dewandaru Christy Maulidiah Daffa Putra Mahardika 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 Gideon Simalango, Yanuar Gunawan, Ayu Lestari Hairah, Ummul Hairah, Ummul Hakim, Muhammad Irvan Hamdani Hamdani . Hamdani Hamdani Hamdani Hamdani Hamdani Hamdani Hamdani Hamdani Hanif, Ahmad Luthfi Hariyanto 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 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 Maya Agustina 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 Rahmat Kamara Raihanfitri Adi Kalipaksi Rajiansyah, Rajiansyah Ramadhaniaty, Dinda Reski Harisma Dewi Barkah Reviansa Fakhruddin Aththar Risky Kurniawan Riswandi Syam Rita Diana 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 Sitania, Farida Djumiati Siti Retno Wulandari Sugandi Sugandi Sumaini Sumaini Supriyono Supriyono Supriyono Supriyono Syaffira Rizky Amalia Taruk, Medi Tejawati, Andi Theresia Amelia Pawitra 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