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All Journal JSI: Jurnal Sistem Informasi (E-Journal) International Conference on Law, Business and Governance (ICon-LBG) FIAT JUSTISIA: Jurnal Ilmu Hukum Jurnal Cita Hukum Seminar Nasional Informatika (SEMNASIF) ICON-CSE Sistemasi: Jurnal Sistem Informasi MODELING: Jurnal Program Studi PGMI Jurnal Sains dan Informatika JURTEKSI Murobbi: Jurnal Ilmu Pendidikan JOISIE (Journal Of Information Systems And Informatics Engineering) Building of Informatics, Technology and Science alfazuna: Jurnal Pembelajaran Bahasa Arab dan Kebahasaaraban Integrated Lab Journal JATI (Jurnal Mahasiswa Teknik Informatika) The Innovation of Social Studies Journal Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Pendidikan dan Teknologi Indonesia Khatulistiwa: Jurnal Ilmu Pendidikan Administrative and Environmental Law Review Muhammadiyah Law Review Journal of Ocean, Mechanical and Aerospace -science and engineering- (JOMAse) Al-MIKRAJ: Jurnal studi Islam dan Humaniora Indonesian Journal Computer Science (ijcs) Jurnal Pengabdian West Science ABDIMASY: Jurnal Pengabdian Kepada Masyarakat Jurnal ilmiah teknologi informasi Asia Jurnal PETISI (Pendidikan Teknologi Informasi) JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Fathoni Fathoni
Fakultas Hukum Unversitas Lampung
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Public Health Social Sciences Other

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COMPARISON OF CLUSTERING MODELS FOR GROUPING LIFESTYLE PATTERNS AND OBESITY FACTORS Al Mas Ud, Khalid; Fathoni, Fathoni; Muhammad Kurniawan, Hafiz
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 12 No. 1 (2025): Desember 2025
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v12i1.4265

Abstract

Abstract: Obesity is an escalating global health concern, with unhealthy lifestyle patterns contributing significantly to its development. This study aims to evaluate and compare three clustering techniques for categorizing lifestyle patterns and obesity-related factors: K-Means, Agglomerative Clustering, and Gaussian Mixture Model (GMM). The data used in this study is sourced from the Food Nutrition dataset, which includes variables such as dietary habits, physical activity, and socio-economic status. The three clustering methods were assessed using evaluation metrics such as Silhouette Score, Davies-Bouldin Index (DBI), and Calinski-Harabasz Index (CHI). The findings revealed that K-Means exhibited the best performance in terms of cluster separation with a Silhouette Score of 0.5559, while GMM showed better flexibility in handling more complex data. Although Agglomerative Clustering produced acceptable results, it had a higher overlap between clusters compared to the other methods. This study offers valuable insights into selecting the most appropriate clustering technique based on the data characteristics. Keywords: agglomerative; clustering; GMM; k-means; lifestyle patterns; obesity Abstrak: Obesitas menjadi masalah kesehatan yang semakin meningkat di seluruh dunia, dengan pola hidup yang tidak sehat berperan besar dalam perkembangannya. Penelitian ini bertujuan untuk membandingkan tiga metode clustering dalam mengelompokkan pola gaya hidup dan faktor yang memengaruhi obesitas, yaitu K-Means, Agglomerative Clustering, dan Gaussian Mixture Model (GMM). Data yang digunakan diperoleh dari dataset Food Nutrition yang mencakup informasi terkait pola makan, aktivitas fisik, serta faktor sosial-ekonomi. Ketiga metode tersebut diuji dengan menggunakan beberapa metrik evaluasi, seperti Silhouette Score, Davies-Bouldin Index (DBI), dan Calinski-Harabasz Index (CHI). Hasil penelitian menunjukkan bahwa K-Means memiliki kinerja terbaik dalam hal pemisahan klaster, dengan nilai Silhouette Score sebesar 0.5559, sementara GMM lebih fleksibel dalam menangani data yang lebih kompleks. Meskipun Agglomerative Clustering memberikan hasil yang dapat diterima, tumpang tindih antar klaster lebih besar dibandingkan dengan kedua metode lainnya. Penelitian ini memberikan pemahaman yang lebih baik mengenai pemilihan metode clustering yang tepat berdasarkan karakteristik data yang digunakan. Kata kunci: agglomerative; clustering; GMM; k-means; obesitas; pola gaya hidup
Analisis dan Perbandingan Akurasi Image Generative AI DALL-E 3 dan Midjourney Menggunakan Metode Frechet Inception Distance (FID) Baidhawi, Alif; Nugraha, Allan; Fathoni, Fathoni; Hendrawan, Deni Agus; Riansyah, M Bintang Naufal; Ibrahim, Ali
JURNAL PETISI (Pendidikan Teknologi Informasi) Vol. 7 No. 1 (2026): JURNAL PETISI (Pendidikan Teknologi Informasi)
Publisher : Universitas Pendidikan Muhammadiyah Sorong

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36232/jurnalpetisi.v7i1.3172

Abstract

Perkembangan teknologi Generative AI telah membawa kemajuan pesat dalam pembuatan gambar berbasis teks, dengan DALL-E 3 dan Midjourney sebagai dua model terdepan. Penelitian ini bertujuan untuk menganalisis dan membandingkan akurasi visual hasil gambar dari kedua model menggunakan metrik Frechet Inception Distance (FID). Lima jenis prompt teks dipilih secara sistematis berdasarkan kategori anjing ras tertentu, dan setiap model menghasilkan 50 gambar yang dibandingkan dengan gambar acuan dari dataset Stanford Dogs. Hasil penelitian menunjukkan bahwa DALL-E 3 memiliki rata-rata skor FID sebesar 19.85, sedangkan Midjourney sebesar 28.42, yang berarti DALL-E 3 menghasilkan gambar yang lebih mendekati visual nyata. Uji statistik menggunakan Independent Sample t-Test menunjukkan adanya perbedaan signifikan antara kedua model. Dengan demikian, DALL-E 3 lebih cocok untuk kebutuhan yang menuntut realisme visual, sedangkan Midjourney lebih unggul dalam eksplorasi artistik.
Analisis Pola Temporal Penyebaran Penyakit DBD dan HIV Berbasis Time Series Clustering Ramadhani, Trie Adriana; Fathoni, Fathoni
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.9262

Abstract

In Indonesia, including in East Java Province, infectious diseases such as Dengue Fever (DHF) and Human Immunodeficiency Virus (HIV) remain public health concerns. Incidence patterns vary by region and time of year. Variations in temporal patterns among districts and cities may lead to suboptimal identification of priority intervention areas when analyses rely solely on absolute case counts. This study aims to analyze the temporal patterns of DHF and HIV case distribution in East Java Province during the 2018–2024 period in order to cluster regions based on similarities in case dynamics over time.The analysis was conducted using a time series clustering approach to group districts and cities according to the similarity of their case development patterns. Temporal similarity was measured using the Dynamic Time Warping method and subsequently clustered using Hierarchical Clustering. Prior to analysis, the data were normalized using the Z-score method to minimize the influence of differences in case scale among regions. The results show that the temporal patterns of DHF and HIV cases were each classified into three main clusters. Cluster quality evaluation using the Silhouette index yielded a value of 0.408 for DHF, indicating a relatively clear cluster structure, whereas a value of 0.197 was obtained for HIV, suggesting a weaker cluster structure due to the complexity and heterogeneity of regional-level case data. Nevertheless, the resulting clusters still provide preliminary information on variations in temporal patterns. The identified clusters represent regions with stable, fluctuating, and increasing case patterns. Several urban areas, such as Pasuruan City, Probolinggo City, and Banyuwangi Regency, tend to belong to clusters with relatively high case levels for more than one disease, indicating challenges in disease control within these regions. These findings provide an initial overview of the temporal dynamics of DHF and HIV cases in East Java, which may serve as supporting evidence for region- and time-based disease control planning.
Crisis Communication in the Digital Age: Analysis of Social Media Strategies for Reputation Recovery in Higher Education Institutions Maulina, Novaria; Setiawan, Herry; Anhar, Vina Yulia; Fathoni, Fathoni; Hamdani, Riky
The Innovation of Social Studies Journal Vol 7, No 2 (2026): The Innovation of Social Studies Journal, March 2026
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/issj.v7i2.17237

Abstract

A communication crisis and public trust that is not managed properly will become a serious threat to institutions because it affects the image and reputation of the organization, including becoming a serious threat to higher education institutions that can impact public trust and organizational sustainability. This study aims to analyze the implementation of effective communication strategies in the era of social media based on SWOT analysis in restoring the reputation of higher education institutions and identifying factors that determine the success of image restoration campaigns. The study uses a qualitative approach with a case study method at a state university in Kalimantan during the period of July-August 2025. Data was collected through crawling using the Brand24 media monitoring application with analysis of social media content, websites and online news portals (via Instagram and TikTok), monitoring of public sentiment using the Brand24 application, and analysis of engagement metrics. Data analysis used triangulation techniques to validate findings from various data sources. The implementation of measurable communication strategies through social media showed significant results with a 71% increase in positive public sentiment and a decrease in negative public sentiment to 4%. On the Instagram social media platform, it achieved an engagement rate of 140.1% with a total reach of 422,423 accounts, while TikTok achieved an engagement rate of 672.3% with 777 thousand views. Fact-based counter-issue strategies and promotion of the Tridharma program have proven effective in changing public perception and restoring public trust. Systematic and measurable social media management using a SWOT approach can be an effective tool in restoring the reputation of higher education institutions. The key to success lies in the consistent dissemination of fact-based content, active engagement with stakeholders, and regular sentiment monitoring.
Pengelompokan Level Hipertensi Berbasis Tekanan Darah Menggunakan Algoritma K-Means Clustering Asoka, Egga; Fathoni, Fathoni; Satria, Hadipurnawan; Anggina, Edith
Jurnal Pendidikan dan Teknologi Indonesia Vol 6 No 3 (2026): JPTI - Maret 2026
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.1477

Abstract

Hipertensi merupakan salah satu komponen utama sindrom metabolik yang berkontribusi signifikan terhadap peningkatan risiko penyakit kardiovaskular. Deteksi dini dan pemetaan tingkat hipertensi menjadi penting untuk mendukung intervensi medis yang tepat. Penelitian ini bertujuan untuk menerapkan algoritma unsupervised learning Algoritma K-Means Clustering dalam mengelompokkan individu berdasarkan parameter tekanan darah sistolik dan diastolik. Dataset yang digunakan terdiri dari 1.878 catatan pasien, yang setelah proses pembersihan data menghasilkan 1.575 data unik. Data distandarisasi menggunakan StandardScaler, dan jumlah klaster optimal ditentukan melalui metode Elbow. Hasil klasterisasi menunjukkan empat klaster utama yang merepresentasikan segmentasi alami tekanan darah, mulai dari tekanan darah rendah hingga tinggi. Visualisasi dua dimensi dan reduksi dimensi menggunakan Principal Component Analysis (PCA) memperlihatkan pemisahan klaster yang relatif jelas. Temuan ini menunjukkan bahwa K-Means mampu mengidentifikasi struktur laten data tekanan darah secara objektif dan berpotensi menjadi dasar pengembangan sistem pendukung keputusan medis berbasis data untuk stratifikasi risiko hipertensi.
Prediksi Lead Scoring untuk Optimasi Penjualan Menggunakan Random Forest dan Teknik SMOTE Pratama Putra, Daffa; Agil Kusuma, Dimas; Al Akbar, M. Rizki; Ibrahim, Ali; Fathoni, Fathoni
JURNAL FASILKOM Vol. 16 No. 1 (2026): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v16i1.11292

Abstract

Accurate lead scoring systems have become a strategic necessity for organizations operating in data-driven marketing environments, as they enable systematic identification of high-value customer prospects to maximize sales conversion efficiency. A fundamental challenge confronting conventional classification models is the class imbalance inherent in real-world marketing data, which induces majority-class bias and substantially reduces sensitivity toward minority-class prospects. This study proposes a Random Forest (RF)-based lead scoring prediction model integrated with the Synthetic Minority Over-sampling Technique (SMOTE) to address this limitation systematically. The dataset employed is the Lead Scoring Dataset from Kaggle, comprising 9,240 customer prospect records from an educational company with a class imbalance ratio of 1.59:1. Preprocessing included missing value treatment, removal of attributes exceeding 40% data loss, mode-based imputation, and categorical feature encoding. Following an 80:20 stratified split, SMOTE was applied exclusively to the training set to produce a balanced class distribution and prevent data leakage. The RF model was configured with n_estimators = 100, max_features = 'sqrt', and class_weight = 'balanced'. The proposed RF+SMOTE model achieved accuracy of 88.80%, precision of 86.44%, recall of 84.13%, F1-Score of 85.27%, and AUC-ROC of 0.9453, outperforming the baseline across four of five evaluation metrics. The most notable improvement was observed in recall, with a gain of 1.26 percentage points. Stratified 5-Fold Cross-Validation confirmed robust generalization capability, with AUC-ROC values consistently ranging between 94% and 95%. These findings demonstrate that the hybrid RF+SMOTE approach effectively enhances high-potential prospect detection while maintaining overall model stability for real-world Customer Relationship Management (CRM) deployment.
Development of a Flask-based Application for Bank Customer Churn Prediction as a Decision Support Tool Wibowo, Suluh Arif; Rezky, Muhammad; Ibrahim, Ali; Afrina, Mira; Fathoni, Fathoni
Sistemasi: Jurnal Sistem Informasi Vol 15, No 4 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i4.6257

Abstract

Customer churn prediction is a crucial aspect of the banking industry for maintaining customer loyalty and reducing the cost of acquiring new customers. This study aims to develop a web-based decision support system capable of predicting potential customer churn using the Gradient Boosting Machine (GBM) algorithm. The dataset used is the Bank Customer Churn Dataset, consisting of 10,000 customer records with 14 attributes. The research stages include exploratory data analysis and preprocessing, which involves data cleaning, categorical feature encoding, feature engineering (BalanceSalaryRatio, TenureByAge, CreditScoreGivenAge), and data balancing using SMOTE to address class imbalance. The GBM model was trained on the balanced dataset and evaluated using accuracy, precision, recall, and F1-score metrics. The evaluation results show that the model achieved an accuracy of 83.95%, with a recall of 67.32% for the churn class, indicating a strong capability in identifying customers at risk of churn. Feature importance analysis reveals that Age and NumOfProducts are the most dominant features, contributing approximately 77% to the prediction. The model was then implemented in a Flask-based web application with an HTML and CSS interface, enabling non-technical users to perform real-time churn predictions. This system is expected to assist banking institutions in designing more targeted and data-driven customer retention strategies.
Klasifikasi Opini Tidak Informatif Pada Program Makan Bergizi Gratis (MBG) Menggunakan Random Forest Syabilla, Lailla Syal; Natasyah, Mei Intan; Fathoni, Fathoni; Siahaan, Jeremiah Alwin
Indonesian Journal Computer Science Vol. 5 No. 1 (2026): April 2026
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/ijcs.v5i1.12509

Abstract

Pemerintahan Prabowo-Gibran meluncurkan kebijakan strategis Program Makan Bergizi Gratis (MBG) untuk menjamin pemenuhan hak dasar anak atas pangan yang aman, sehat, dan bergizi. Urgensi program ini didasarkan pada angka stunting di Indonesia tahun 2024 yang mencapai 14%, sehingga peluncuran kebijakan Program Makan Bergizi Gratis (MBG) memicu diskusi publik yang masif di platform media sosial X dengan jumlah pengguna mencapai 24,7 juta orang. Namun, volume data yang besar tersebut menghadirkan masalah "Data Sampah" (Noise) berupa spam, promosi jualan, hingga akun bot yang berpotensi menyebabkan bias pada analisis opini publik terhadap program Program Makan Bergizi Gratis (MBG). Penelitian ini bertujuan membangun model klasifikasi opini tidak informatif dengan mengimplementasikan algoritma Random Forest berbasis Knowledge Discovery in Database (KDD) sebagai tahap pra-pemrosesan sebelum analisis sentimen lanjutan. Data yang digunakan berjumlah 10.000 tweet bersumber dari Kaggle, diproses melalui lima tahapan KDD meliputi Data Selection, Data Preprocessing, Data Transformation, Data Mining, dan Data Evaluation dengan menggunakan RapidMiner. Representasi fitur dilakukan dengan pembobotan TF-IDF dan validasi model menggunakan k-fold Cross Validation dengan k=10. Hasil evaluasi menunjukkan model mencapai akurasi 82,03%, precision 93,78%, recall 68,61%, dan F-Measure 79,24%. Hasil ini membuktikan bahwa pendekatan KDD berbasis Random Forest efektif digunakan sebagai pipeline filter noise yang terstruktur untuk teks media sosial berbahasa Indonesia, khususnya pada domain opini kebijakan pemerintah.
Co-Authors Abdul Aziz Zaenal Buchori Acta, Muhammad Fakhri Nadrota Adeliani, Adeliani Aditya Ainul Haqiqi Agil Kusuma, Dimas Agus Pracoyo Akbar Kurniawan, Iqbal Akrom, Muhammad Adib Al Akbar, M. Rizki Al Mas Ud, Khalid Aldhy Rizhaldy S.G Alghifari, Muhammad Ali Ibrahim Alifayoezra, Muhammad Dzaky Amelia Amelia Andriani Parastiwi Angelina Tompunu, Keisha Anggina, Edith Apriansyah Putra Arba'i, Sultan Ari Wedhasmara Arinie, Putri Mutiara Asyiq, Abdulloh Athallah, Deni Aulia, Cantika Auliya, Lana Nur Azmi Zaky, Muhammad Baidhawi, Alif Dedy Kurniawan Demetria, Putri Dewi Aprilliana Aprilliana Donny Radianto Dwiyansyah, Octa Egga Asoka Eka Afrianti Eka Saputra Fachry Abda El Rahman Faiq, Al Ikhsan Fauzi, Muhamad Rizal Febriansyah, Dian Firman Muntaqo Gurruh Dwi Septano Hadipurnawan Satria Hamdani, Riky Hariza Marshella, Siti Hendrawan, Deni Agus Heni Siswanto Herman Hariyadi Ari Murtono Herry Setiawan Hieronymus Soerjatisnanta Huda, Hisbullah Ikbal Ikbal Inda Kesuma S Istiqomah, Amalia Windy James Reinaldo Jodi Pratama, Muhammad Komarudin Achmad Luh Putu Ratna Sundari M Baihaqi M. Alfanshuril Hakim Maharani Maharani, Maharani Maroni Maroni Masrury, Farhan Maulina, Novaria Mira Afrina Mohammad Khalid Mohammad Luqman Muarif, Moh. Syamsul Muhammad Adryan Munir Rifa'i Muhammad Akib Muhammad Dzulkifli Muhammad Kurniawan, Hafiz Muhammad Naufal Suhaimi Nabilatulrahmah, Raihana Natasyah, Mei Intan Nicky Andre Prabatama NIZAR, MOHAMMAD Nugraha, Allan Patma, Tundung Subali Pratama Putra, Daffa Putri, Salsanabila Mariestiara Putri, Septhia Charenda Rachmad, Muhammad Ichsan Farrel Rahman, Muhammad Fadhil Rahmat Izwan Heroza Raihana Putri, Naila Ramadhani, Trie Adriana Rezky, Muhammad Riansyah, M Bintang Naufal Risyahputri, Aliyananda Rizka Mumtaz, Fadia Rizki, Raditya Dafa Rulyanti Dyah Prawesti Saimi, Saimi Salsa Kinanty, Reina Setia, Arvhi Randita Siahaan, Jeremiah Alwin Sidik Nurcahyo Siswoko Siswoko Sofuan Jauhari Sony Oktapriandi Sriwijaya, Sayid Bahri Suci Fitriani, Suci Syabilla, Lailla Syal Syahputra Zaki, Imam Syahrul Akhmal Hidayatulloh Tammam, Bimmo Fathin Tarmukan Tarmukan Therina Lakeisyah, Eka Tri Alfandy, Muhammad Vina Yulia Anhar Wahyu Tri Wahono Waton, Muhammad Nasrul Wibowo, Suluh Arif Widia Wahyuningtyas F Winarno, Totok Yanis Alhafidz Akhmad Yudith Mimbar Ali Sakti Yulianto Yulianto Zahrona Arifatul Maula