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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 International Journal of Advances in Intelligent Informatics Jurnal Informatika dan Teknik Elektro Terapan MUSTEK ANIM HA Scientific Journal of Informatics Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika 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 Scientific Journal of Informatics Journal of Innovation Information Technology and Application (JINITA) Indonesian Journal of Data and Science 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) Jurnal Sains Teknologi dan Sistem Informasi 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 Information Technology Education Journal Journal Of Artificial Intelligence And Software Engineering Jurnal INFOTEL Journal of Computer Science Contributions (Jucosco) Journal of Computer Science and Information Technology Inovasi Teknologi Masyarakat Jurnal Pengabdian Siliwangi International Journal of Applied Mathematics and Computing.
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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Klasifikasi Status Gizi Balita Menggunakan Metode K-Nearest Neighbor Syifani, Sarah; Septiarini, Anindita; Taruk, Medi; Wati, Masna; Tejawati, Andi
Jurnal Rekayasa Teknologi Informasi (JURTI) Vol 10, No 1 (2026): Jurnal Rekayasa Teknologi Informasi (JURTI)
Publisher : Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/jurti.v10i1.25641

Abstract

Status gizi balita merupakan indikator penting dalam menilai tingkat kesehatan anak yang dapat diketahui melalui pemeriksaan antropometri. Berdasarkan data Survei Status Gizi Indonesia (SSGI) tahun 2022, prevalensi gizi kurang di Provinsi Kalimantan Timur mencapai 23,9%, khususnya di Kota Samarinda sebesar 25,3%, yang menunjukkan bahwa masalah gizi masih perlu mendapatkan perhatian. Penelitian ini bertujuan untuk mengklasifikasikan status gizi balita menggunakan metode K-Nearest Neighbor (KNN) dengan variasi nilai K dan rumus jarak yang berbeda guna memperoleh performa terbaik. Data yang digunakan merupakan data sekunder dari Puskesmas Pasundan, Kota Samarinda, sebanyak 760 data balita usia 0–60 bulan. Tahapan penelitian meliputi pengumpulan data, perancangan data melalui proses preprocessing (data selection, penanganan outlier, dan transformasi data menggunakan min-max normalization serta encoding), implementasi metode KNN dengan variasi nilai K (1, 3, 5, 7, 9, 11) dan rumus jarak (Euclidean, Manhattan, Minkowski), serta evaluasi model menggunakan Confusion Matrix Multiclass. Berdasarkan hasil pengujian, akurasi tertinggi diperoleh sebesar 89,24% dengan nilai presisi 66,29%, recall 63,70%, dan F1-score 63,12% menggunakan nilai K = 1 dan rumus jarak Euclidean. Hasil ini menunjukkan bahwa metode KNN mampu memberikan performa klasifikasi yang baik dalam menentukan status gizi balita berdasarkan atribut usia, jenis kelamin, berat badan, dan tinggi badan.
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.
Peningkatan Literasi Artificial Intelligence untuk Mendukung Belajar dan Produktivitas melalui Webinar Nasional Kolaborasi Perguruan Tinggi Herlawati; Rahmadya Trias Handayanto; Rakhmat Purnomo; Anindita Septiarini
Journal Of Computer Science Contributions (JUCOSCO) Vol 6 No 2 (2026): Juli 2026
Publisher : Lembaga Penelitian, Pengabdian kepada Masyarakat dan Publikasi Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/5cb89a62

Abstract

The rapid advancement of Artificial Intelligence (AI) has significantly transformed education and professional practices. However, maximizing the benefits of AI requires adequate digital literacy to ensure its effective, ethical, and responsible use. This community engagement program aimed to improve Artificial Intelligence literacy for learning and productivity through a National Collaborative Webinar involving multiple higher education institutions. The webinar was conducted online via Zoom Meeting on May 23, 2026, involving 345 participants from universities, schools, and other institutions across eight provinces in Indonesia. The implementation consisted of planning, material development, webinar promotion, pre-test, interactive presentations, and hands-on practice using AI applications such as ChatGPT, Claude, and Mendeley AI, followed by a post-test and participant satisfaction evaluation. The results demonstrated that the webinar successfully reached participants from diverse professional and institutional backgrounds. The comparison between pre-test and post-test results indicated improvements in most evaluation indicators, particularly participants’ intention to continuously use AI in learning and daily work, which increased from 35.7% to 41.9%. Participants’ ability to critically evaluate AI-generated outputs also increased from 43.2% to 44.8%, while the perception that AI improves task efficiency increased from 46.4% to 47.0%. These findings indicate that a collaborative national webinar is an effective community engagement approach to enhancing AI literacy while encouraging the productive and responsible adoption of AI technologies in education and professional activities.
ANALISIS EXPLAINABLE AI PADA PERBANDINGAN MODEL XGBOOST DAN LOGISTIC REGRESSION UNTUK CREDIT SCORING Ari Fullah; Fathir Januarta; Vashih Al Farizi Farizi; Muhammad Nashrul Fakhri; Anindita Septiarini; Joan Angelina Widians; Akhmad Irsyad
Jurnal Informatika dan Teknik Elektro Terapan Vol. 14 No. 3 (2026)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v14i3.9842

Abstract

Perkembangan machine learning dalam industri keuangan mendorong penggunaan model prediktif yang semakin kompleks untuk penilaian risiko kredit. Meskipun model seperti XGBoost menawarkan akurasi tinggi, tantangan utama yang muncul adalah kurangnya transparansi dalam pengambilan keputusan. Penelitian ini bertujuan membandingkan performa model XGBoost dan Logistic Regression dalam prediksi credit scoring, serta menganalisis interpretabilitas kedua model menggunakan metode SHAP (SHapley Additive exPlanations). Data yang digunakan adalah Credit Risk Dataset dari platform Kaggle yang terdiri atas 32.581 observasi dengan 11 variabel independen dan 1 variabel target. Tahapan penelitian meliputi preprocessing data, pemodelan, evaluasi, dan analisis SHAP. Hasil evaluasi menunjukkan XGBoost mengungguli Logistic Regression dengan Accuracy 0,9122 berbanding 0,8104, F1-Score 0,7956 berbanding 0,6356, dan ROC AUC 0,9458 berbanding 0,8635. Analisis SHAP mengungkap bahwa loan_percent_income dan loan_int_rate merupakan fitur paling berpengaruh pada kedua model, konsisten dengan konsep Debt-to-Income Ratio dalam analisis kredit konvensional. Meskipun XGBoost unggul secara prediktif, SHAP terbukti mampu menjelaskan mekanisme keputusan kedua model secara transparan. Penelitian ini menunjukkan bahwa kombinasi XGBoost dan SHAP merupakan pendekatan yang direkomendasikan untuk pengembangan sistem credit scoring yang akurat sekaligus dapat dipertanggungjawabkan.
Food Delivery Time Prediction using Tree-Based Ensemble Models: A Comparative Study with Explainable Artificial Intelligence Raihanfitri Adi Kalipaksi; Haviluddin Haviluddin; Anindita Septiarini; Joan Angelina Widians; Novianti Puspitasari
Information Technology Education Journal Vol. 5, No. 3, August (2026)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/intec.v5i3.12157

Abstract

Purpose – Being able to predict delivery time accurately is important for online food delivery services, both for operational efficiency and customer satisfaction. However, this is not an easy task. Delivery time depends on many factors that are related to each other, such as courier characteristics, delivery distance, and order-related information, and these factors interact in complex ways. This study looks at how well tree-based ensemble learning models can predict food delivery time, and also uses explainable artificial intelligence so the models can still be interpreted properly. Design – This study uses 45,593 delivery records taken from Kaggle. In this study, four tree-based ensemble models were developed, namely Random Forest, Gradient Boosting, XGBoost, and LightGBM, with each model optimized through hyperparameter tuning. The models were evaluated using repeated 5-fold cross-validation with three repetitions, and their performance was assessed using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R². Findings – LightGBM Tuned came out with the best numerical performance, showing an MAE of 5.678 minutes, RMSE of 7.204 minutes, and R² of 0.411. However, based on ANOVA and Tukey's post-hoc test, the difference in performance between the best boosting-based models was not statistically significant. SHAP analysis also showed that courier rating, delivery distance, courier age, and several interaction features were the factors that had the biggest influence on delivery time prediction. Research implications – The findings suggest that boosting-based ensemble learning models can provide moderate predictive performance while offering interpretable insights into the factors contributing to delivery time predictions. Nevertheless, the moderate R² value indicates that additional operational variables, such as traffic conditions, restaurant preparation time, and courier workload, may be required to improve practical prediction reliability. Originality/value – This study combines ensemble learning, feature engineering, statistical validation, and explainable artificial intelligence together to evaluate both the predictive performance and interpretability of models for food delivery time prediction. 
Detecting Gambling SEO Spam on Academic Domains using Structural and Textual Features: A Domain-Holdout Evaluation Muh Ghazy Daffa Sampe; Muhammad Kevin Adli Pratama; Muhamad Rayhan Akhsani Taqwim; Kalingga Dwindra Putraka; Anindita Septiarini; Novianti Puspitasari
SISTEMASI Vol 15, No 8 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i8.6457

Abstract

SEO spam practices related to gambling on Indonesian academic domains pose dual risks: they undermine the integrity of institutional websites and impose an operational burden on security teams tasked with distinguishing compromised pages from legitimate content. This study develops a supervised detection approach to identify gambling-related SEO spam pages on .ac.id domains using a rigorously curated reference dataset. The final dataset contains 2,003 manually annotated pages collected from 938 unique domains, comprising 815 malicious pages and 1,188 benign pages. The proposed pipeline combines textual signals from character-level TF-IDF with structural page indicators, including hidden elements, suspicious links, iframes, and external-link patterns. To avoid overly optimistic performance estimates, evaluation was conducted using a domain-holdout protocol, in which 20% of the domains were completely excluded from model training and selection. Five models were compared: a keyword-based baseline, Naïve Bayes with word-level TF-IDF, Random Forest with structural features, and hybrid Logistic Regression and Support Vector Machine (SVM) models. Experimental results on the holdout set show that Random Forest with structural features achieved the highest F1-score of 0.8844, whereas the proposed hybrid SVM achieved the highest precision of 0.9205, with an F1-score of 0.8663. These findings indicate that structural compromise signals are more robust than textual cues in detecting stealthy SEO spam scenarios, while hybrid models remain promising when high precision and interpretability are prioritized.
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 Akhmad Syaifudin, Encik Alameka, Faza Aldi Daffa Arisyi Alfrina Mewengkang Alif Rifa’i Alvito Gabbriel Saputra Alyani Noor Septalia Amalia, Syaffira Rizky Ambari, Nasser Ambon, Matelda Yunanta Andi Tejawati Andri Syafrianto Anita Ahmad Kasim Annisa Putri Novalianti Anton Prafanto Antonieta Aryuka Paskalia Nggotu Ardi Setyiawan Ari Fullah Arif Hidayat Arindra Nurshadrina Ramadini Arini Wijayanti Asmita, Rizka Aulia Rahman Awang Harsa Kridalaksana Awang Zheri Rhesvianur Az Zahrah, Rezha Nur Bandhaso, Victor Bima Prihasto Briyan Efflin Syahputra Budi Rahmani Budiman, Edy Cakra Dewandaru Chairunnisa Ardiansyah Lamasitudju Christy Maulidiah Daffa Putra Mahardika Didit Suprihanto, Didit Dwi Prasetio Dyna Marisa Khairina Edy Winarno Enny Itje Sela Ery Burhandenny, Aji Ery Burhandeny, Aji Evi Wildana Fahrozi, Muhammad Naufal Fairil Anwar Fajri, Muhamad Mushfa Hikmatal Fandi Alief Al Akbar Farida Djumiati Sitania Fathia Nuq Qamarina Fathir Januarta 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 Harry Tanni Pagiu Hatta, Heliza Rahmania Haviluddin Haviluddin Haviuddin, Haviluddin Heliza Hatta Heliza Rahmania Hatta, Heliza Rahmania Henderi . Heni Sulastri Herlawati Herlawati 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 Irsyad, Akhmad Kalingga Dwindra Putraka Kalingga Dwindra Putraka Kamila, Vina Zahrotun Kiki Purwanti Laraswati, Sherina Lempas, Gidion Lili, Juniver Veronika Lukman Nadjamuddin M. Rizky Nilzamyahya Maharani, Agustina Dwi Mahendra, Dicky Alvian Masa, Amin Padmo Azam Masna Wati Maya Agustina Medi Taruk Muh Ghazy Daffa Sampe Muhamad Azhari Muhamad Rayhan Akhsani Taqwim Muhammad Abdillah Muhammad Abdillah Muhammad Aidil Saputra Muhammad Andas Lesmana Muhammad Bakri Muhammad Dzacky Muhammad Ifandi Muhammad Kevin Adli Pratama Muhammad Nashrul Fakhri Muhammad Nur Ramadhan Muhammad Rafif Hanif Muhammad Sofian Sauri Mu’nisah Assisi Najwa Felira Zetti Nanda Arianto Nathaniela Aptanta Parama Nggotu, Antonieta Aryuka Paskalia Novi Puspitasari 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 Puguh Budi Prakoso Puspitasari, Novianti Puspitasari, Novitanti Putra Ramdani, Aditya Putri, Septi Aulia Rafi Ichsanul Iqbal Rahmadya Trias Handayanto Rahmat Kamara Raihanfitri Adi Kalipaksi Raihanfitri Adi Kalipaksi Rajiansyah, Rajiansyah Rakhmat Purnomo Ramadhaniaty, Dinda Raudhya Azzahra Reski Harisma Dewi Barkah Reviansa Fakhruddin Aththar Ricky Anggari Risky Kurniawan Riswandi Syam Rita Diana Riyayatsyah, Riyayatsyah Rizqi Saputra Rohman, Reisa Maulidya 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 Sophy Awaliah Sugandi Sugandi Sumaini Sumaini Supriyono Supriyono Supriyono Supriyono Surya Eka Priyatna Syaffira Rizky Amalia Syifani, Sarah Tejawati, Andi Theresia Amelia Pawitra Tulili, Hadie Pratama Ummul Hairah Ummul Hairah Vashih Al Farizi Farizi 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 Yuyun Nabilawati Rumbia zahra salsabila Zainal Arifin Zulfariansyah, Muhammad