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All Journal Proceedings Konferensi Nasional Sistem dan Informatika (KNS&I) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Ilmiah Matrik Journal of Education and Instruction (JOEAI) METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Indonesian Journal of Applied Informatics CSRID (Computer Science Research and Its Development Journal) Journal of Information Systems and Informatics IT (INFORMATIC TECHNIQUE) JOURNAL JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) Jurnal Ilmiah Intech : Information Technology Journal of UMUS Jurnal Dialektika Informatika (Detika) Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat Journal Automation Computer Information System (JACIS) Abdiformatika: Jurnal Pengabdian Masyarakat Informatika The Indonesian Journal of Computer Science Research Formosa Journal of Computer and Information Science JAMU : Jurnal Abdi Masyarakat UMUS Journal of Computing Theories and Applications Fahma : Jurnal Informatika Komputer, Bisnis dan Manajemen Intechno Journal : Information Technology Journal
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Fakultas Ilmu Komputer, Universitas Amikom Yogyakarta
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Sentimen Analis Thread Investasi Pada Twitter Menggunakan Naïve Bayes Hartatik Hartatik
The Indonesian Journal of Computer Science Research Vol. 1 No. 1 (2022): Januari
Publisher : Hemispheres Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59095/ijcsr.v1i1.13

Abstract

Social media is one of the market shares that allows people to consume news or promotions from an investment service because the source data is abundant and is updated every second on the internet. Social media is vulnerable to containing news or information on an investment platform and other forms of investment that are illegal or called fraudulent investments, so to overcome this, an Investment Thread Analyst Sentiment on Twitter is needed. The method used to evaluate investment threads to obtain positive and negative feedback is nave Bayes. The research results received a precision score of 0.69, recall of 0.70, f1-score (f-measure) of 0.74, and accuracy of 0.7.
Comparative Analysis of Traditional Machine Learning and Sequential Deep Learning Models for Spam Email Classification Harliana Harliana; Hartatik Hartatik; Achmad Alvi Yudanuari
Formosa Journal of Computer and Information Science Vol. 5 No. 1 (2026): March 2026
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/fjcis.v5i1.16502

Abstract

This study compares the performance of traditional machine learning methods and sequential deep learning models for text-based spam classification. The primary issue addressed is the lack of consistent, fair evaluation across these approaches due to variations in datasets, preprocessing techniques, and experimental settings across previous studies. To overcome this limitation, this research proposes a controlled comparative evaluation framework by employing a unified dataset, standardized preprocessing procedures, consistent data splitting, and identical evaluation metrics. The dataset used consists of 5,572 messages with an imbalanced class distribution; therefore, oversampling was applied to the training data to mitigate bias. The evaluated models include TF-IDF-based Logistic Regression as the baseline, as well as Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRUs) as deep learning models.
Rancang Bangun Sistem Antrian Terkustomisasi Berbasis Android Yoeseph, Nanang Maulana; Riasti, Berliana Kusuma; Hartatik, Hartatik; Pratisto, Eko Harry; A'la, Fiddin Yusfida
IJAI (Indonesian Journal of Applied Informatics) Vol 6, No 1 (2021)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ijai.v6i1.56778

Abstract

Abstrak : Sebagian besar pelayanan publik di era ini masih menggunakan sistem konvensional. Yang mana, klien layanan mendapatkan tiket antrean, menunggu, dan dilayani di tempat yang sama. Penelitian ini bertujuan untuk memudahkan dan memungkinkan orang untuk mengantre dari jarak jauh. Dengan demikian waktu yang awalnya digunakan untuk dihabiskan menunggu, bisa digunakan untuk dihabiskan melakukan sesuatu yang lain lebih berguna.Berdasarkan kondisi yang dikatakan di atas, aplikasi yang menghubungkan agen layanan dengan klien layanan perlu dibuat. Aplikasi ini memanfaatkan internet dan smartphone yang dapat diakses melalui aplikasi Android atau browser web. Pengembangan aplikasi ini menggunakan kerangka kerja Ionic React. Aplikasi ini dirancang dan dibangun menggunakan metode Waterfall yang terdiri dari pengamatan dan pengumpulan data, analisis, desain sistem, bangunan dan pengujian, kesimpulan dan saran.Dari desain dan bangunan yang telah dilakukan, dibuat aplikasi yang memiliki ftur dasar untuk antrean online. Aplikasi ini dapat dijalankan di browser web dan perangkat Android dengan sistem operasi minimum Android 4.4 KitKat.Abstract : Most public services in this era still use conventional systems. Which is, service clients get queue tickets, wait, and be served in the same place. This research aims to ease and enable people to queue remotely. Thus the time that is originally used to be spent waiting, could be used to be spent doing something else more useful. Based on the conditions said above, an application that connects service agencies with service clients needs to be made. This application utilizes the internet and smartphone which can be accessed through Android application or web browser. The development of this application uses the Ionic React framework. This app is designed and built using the Waterfall method consisting of observation and data collection, analysis, system design, building and testing, conclusion and suggestion.
Explainable AI for Water Quality Classification Using Ensemble Stacking Windha MP Dhuhita; Hastari Utama; Hartatik; Bayu Setiaji; Haryoko
Journal of Information System and Informatics Vol 8 No 3 (2026): June
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i3.1601

Abstract

This study proposes a robust and interpretable machine learning framework for water quality classification using a publicly available water quality dataset containing 7,996 samples and 20 physicochemical features with an imbalanced class distribution (88.59% majority and 11.41% minority). The study addresses the critical issue of biased classification toward the majority class, which can lead to risk-prone misclassification of unsafe water. An ensemble stacking model combining XGBoost, LightGBM, and CatBoost with a Random Forest meta-learner (passthrough) was developed using an anti-leakage pipeline integrating RobustScaler and SMOTE within stratified 80:20 train–test cross-validation, while hyperparameter tuning was optimized using F1-score to improve minority-class performance; SHAP was further applied for global and local explainability. The proposed model achieved an F1-score of 0.8563 for the minority class and a ROC-AUC of 0.9846, indicating strong discriminative performance, while SHAP analysis identified ammonia as the most influential feature and revealed that False Negative errors were mainly caused by complex feature interactions. The study contributes an integrated framework combining stacking ensemble learning, anti-leakage evaluation, and SHAP-based global–local interpretation to support more reliable and transparent water quality classification; however, the findings are currently limited to a single dataset and and require multi-dataset validation.
Peningkatan Ketepatan Makharijul Huruf melalui Media Pembelajaran Digital Berbasis Android di TPA Masjid Diponegoro Hartatik Hartatik; Supriatin Supriatin; Wiwi Widayani; Irma Rofni Wulandari; Ninik Tri Hartanti
Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat Vol. 6 No. 1 (2026): Januari 2026 - Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/altifani.v6i1.973

Abstract

Kegiatan pengabdian masyarakat ini dilaksanakan di TPA Masjid Diponegoro, dengan tujuan meningkatkan ketepatan makhrijul urf santri tingkat pemula hingga menengah melalui media pembelajaran digital berbasis Android. Permasalahan utama yang dihadapi adalah ketidaktepatan pelafalan huruf, keterbatasan waktu tatap muka, dan minimnya latihan mandiri di rumah. Solusi yang ditawarkan berupa pengembangan aplikasi FlutterAndroid yang memuat 84 audio huruf hijaiyah dengan tiga harakat, ilustrasi makhraj interaktif, serta gim drag-and-drop dengan umpan balik instan. Metode pelaksanaan mencakup pelatihan guru, implementasi pembelajaran audiovisual di kelas, dan latihan micro-learning harian yang dipantau melalui ceklist orang tua. Hasil menunjukkan peningkatan rerata skor artikulasi sebesar 0,60,8 poin (skala 14) dan ukuran efek tinggi (Cohens d > 1,5). Program ini efektif meningkatkan kemampuan artikulasi, kemandirian belajar, dan standarisasi pengajaran, serta layak direplikasi di TPQ lain.
Beyond Binary Fraud Detection: Amount-Aware Operational Ranking for Transaction Risk Prioritization Hartatik Hartatik
Journal of Computing Theories and Applications Vol. 4 No. 1 (2026): JCTA 4(1) 2026
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.16260

Abstract

Fraud detection in digital financial transactions is traditionally formulated as a binary classification problem, although real-world fraud investigation requires analysts to prioritize a limited number of suspicious transactions according to operational risk and potential financial impact. This study reformulates fraud detection as an amount-aware operational ranking problem for fraud-risk prioritization. Transactions are organized into time-window query groups, and fraudulent transactions are assigned graded relevance based on training-only transaction-amount quartiles, enabling the ranking objective to distinguish low- and high-severity fraud without relying on proprietary cost matrices. The proposed formulation is implemented using a representative Learning-to-Rank framework based on LambdaMART, while an out-of-fold XGBoost risk score is incorporated as an auxiliary feature to refine the ranking representation rather than serve as the primary contribution. Experiments conducted on a public credit-card fraud dataset using chronological validation and future-holdout testing demonstrate that amount-aware relevance consistently improves severity-aware top-rank ordering compared with conventional binary relevance. The proposed HybridLTR_amount model significantly outperforms XGBClassifier and PureLTR_binary in terms of all-query NDCG@10, whereas its performance is not statistically different from PureLTR_amount, indicating that the primary empirical improvement is attributable to the amount-aware ranking formulation rather than the auxiliary hybrid component. Additional operational analyses show that high-risk transactions and fraudulent financial losses are concentrated within a compact top-ranked segment, while budget-oriented evaluation demonstrates the practical value of the proposed formulation under limited analyst review capacity. These findings establish amount-aware operational ranking as an effective formulation-centric framework for operational fraud-risk prioritization rather than as a new classification algorithm.
Implementasi Algoritma Naïve Bayes Dalam Mengidentifikasi Jenis Penyakit Cacar Dengan Image Processing Yudha Bagas Pattimura; Melcior Paitin Kanoena; Anggit Dwi Hartanto; Hartatik Hartatik; Kusnawi Kusnawi
Intechno Journal : Information Technology Journal Vol. 5 No. 1 (2023): July
Publisher : Universitas AMIKOM Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/intechnojournal.2023v5i1.1571

Abstract

Cacar merupakan salah satu penyakit kulit yang sering diderita banyak masyarakat mulai dari anak bayi sampai orang tua. Cacar memliki beberapa jenis yang antara lain adalah cacar air (Quipperian), cacar api (herpes zoster) dan cacar monyet, seluruh penyakit ini semuanya dapat menular ke seama manusia melalui kontak lansung, bersin, batuk atau tersentuh dengan isi gelembung cacar yang pecah. Minimnya pengetahuan masyarakat dan tidak adanya penyuluahan dari pemerintah membuat masyarakat tidak mengetahui akan perbedaan jenis-jenis cacar yang diderita dan dapat terjadinya kesalahan dalam pengobatan. Dalam penilitian ini kami menggunakan image processing dengan metode histogram untuk ekstraksi fitur tekstur cacar tersebut serta menggunakan dengan metode klasifikasi naïve bayes dalam mengklasifikasi jenis cacar yang diderita oleh pasien. Dari penilitian yang kami lakukan menunjukan bahwa mengklasifikasi nilai ekstraksi fitur tekstur citra cacar dengan metode naïve bayes memperolehonilai akurasi sebesar 75%.
Benchmarking Ensemble Learning Berbasis Imbalance - Aware Untuk Prediksi Mortalitas Hantavirus Pada Data Surveilans Simulatif Heru Vitono; Yahya Abdul Aziz; Suratmi Suratmi; Yohannes Yohannes; Hartatik Hartatik
Journal Automation Computer Information System Vol. 6 No. 2 (2026): November (In Progress Issue)
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/jacis.v6i2.212

Abstract

Hantavirus merupakan penyakit zoonosis dengan angka fatalitas tinggi (30–50%), sementara metode prognosis klinis yang tersedia masih terbatas dalam memperkirakan keselamatan pasien secara individual. Studi ini mengembangkan kerangka benchmarking ensemble learning yang sadar-ketidakseimbangan (imbalance-aware) untuk memprediksi risiko kematian pasien Hantavirus berdasarkan data klinis pada saat pasien tiba di rumah sakit. Data yang digunakan berupa dataset simulatif surveilans Hantavirus global sebanyak 2.000 kasus yang mencakup sepuluh negara dan lima strain virus, dengan komposisi kelas yang sangat timpang (proporsi kematian hanya 7,75%). Dua algoritma ensemble, yakni Random Forest dan XGBoost, dilatih dengan optimasi hiperparameter Bayesian dan dipadukan dengan teknik hibrida SMOTE-Tomek untuk mengatasi ketimpangan kelas. Penelitian bertujuan menguji efektivitas SMOTE-Tomek pada data latih, membandingkan kinerja Random Forest dan XGBoost pada kondisi data timpang, serta mengidentifikasi faktor penentu prediksi melalui Feature Importance dan SHAP. Hasil memperlihatkan Random Forest yang dipadukan SMOTE-Tomek memberikan performa paling stabil (akurasi 85%, F1-Score kelas minoritas 0,1892, dan MCC 0,1107) dan unggul dibanding XGBoost. Analisis SHAP menempatkan usia pasien serta durasi karantina sebagai fitur paling berpengaruh. Temuan ini menegaskan pentingnya penanganan ketimpangan kelas berbasis resampling hibrida untuk membangun model prediksi mortalitas yang baik sekaligus interpretabel sebagai penunjang keputusan klinis
Co-Authors A'la, Fiddin Yusfida Abdul Mizwar A. Rahim Achmad Alvi Yudanuari Afdhal Walidy Al Abdul Rohim Aliyani, Ade Surya Ananda, Fadhilah Dwi Andri Syafrianto Anggit Dwi Hartanto Anggit Dwi Hartanto Anggit Dwi Hartanto, Anggit Dwi Arifa Satria Dwi Cahya Bagas Satrio Utomo Bayu Setiaji Berliana Kusuma Riasti Dwi Lestari Eko Harry Pratisto Ema Utami Eva Suhailah Faishal Arif Wibowo Feskariani, Dwi Galih Candra Adi Saputra Harliana Harliana Haryoko Heru Vitono Irma Rofni Wulandari Kencana, Adi Kresna Khairullah, Irfan Khalil Krisnawati Krisnawati Kurniawan, Aas Andri Kusnawi Kusnawi Lucky Adhikrisna Wirasakti Melcior Paitin Kanoena Muhammad Aldi Muhammad Enggar Aziz Hibbannuari Nanang Maulana Yoeseph NINIK TRI HARTANTI Nur Safariatun Purwandari, Elce R.M.Herdian Bhakti Rahmadita Sabrina Islahany Ramadhani, Zanuarestu Rony Permadi Rosyid Rosyid Safira, Livia Setiadi F., A. Farhan Shintya Wulan Safitri Sri Devi Nurhayati Sri Yanti Supriatin Supriatin Suratmi Suratmi Syafrianto, Syafrianto Taufiq Nashrullah Utama, Hastari Windha MP Dhuhita Wiwi Widayani Yahya Abdul Aziz Yohannes Yohannes Yudha Bagas Pattimura Yusa, Aldo