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All Journal Prosiding Semnastek Indonesian Journal on Software Engineering (IJSE) Jurnal Teknik Komputer AMIK BSI Paradigma Bina Insani ICT Journal Jurnal Sisfokom (Sistem Informasi dan Komputer) JPP IPTEK (Jurnal Pengabdian dan Penerapan IPTEK) Jurnal Mantik JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Jurnal Infortech JPM: JURNAL PENGABDIAN MASYARAKAT Journal of Informatics Management and Information Technology KLIK: Kajian Ilmiah Informatika dan Komputer Computer Science (CO-SCIENCE) Reputasi: Jurnal Rekayasa Perangkat Lunak IMTechno: Journal of Industrial Management and Technology JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Administrasi Kantor Info Abdi Cendekia Journal of Accounting Information System Bulletin of Informatics and Data Science Jurnal Sains dan Teknologi Informasi Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Komtika (Komputasi dan Informatika) Jurnal Teknoinfo Jurnal Komputer, Informasi dan Teknologi Journal of Decision Support Systems and Multi-Criteria Decision Making (JODESMA)
Titik Misriati
Universitas Bina Sarana Informatika
Religion Aerospace Engineering Humanities Astronomy Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Medicine & Pharmacology Public Health Social Sciences Transportation Other

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OPTIMASI KLASIFIKASI GANGGUAN TIDUR PADA DATASET TIDAK SEIMBANG MENGGUNAKAN SMOTE DAN ALGORITMA MACHINE LEARNING Titik Misriati; Riska Aryanti
Jurnal Teknoinfo Vol. 19 No. 2 (2025): July 2025 Period
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/teknoinfo.v19i2.295

Abstract

Sleep disorders are increasingly prevalent health issues that significantly affect individual’s quality of life. Timely detection and accurate classification of these disorders are essential for proper diagnosis and effective clinical intervention. However, a major challenge in classifying sleep disorders lies in the imbalance of data distribution—where majority classes have substantially more data than minority ones. This imbalance often leads to predictive models that favor the dominant class, thereby reducing overall classification accuracy. This study focuses on enhancing sleep disorder classification performance on imbalanced datasets by applying the Synthetic Minority Over-sampling Technique (SMOTE) to balance the data. It also evaluates the effectiveness of various machine learning algorithms in identifying sleep disorders. The algorithms analyzed include Random Forest (RF), Neural Network (NN), Naive Bayes (NB), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Logistic Regression (LR), tested both before and after applying SMOTE. Model performance was assessed using accuracy, precision, recall, and F1-score to ensure a comprehensive evaluation. The findings indicate that SMOTE consistently boosts the performance of all tested models. Among them, the Neural Network combined with SMOTE achieved the highest performance, with an accuracy of 92.00%, precision of 91.88%, recall of 92.00%, and an F1-score of 91.91%. Additionally, the Random Forest model with SMOTE produced the highest F1-score at 93.18%, demonstrating strong performance stability. These results highlight the effectiveness of integrating oversampling techniques like SMOTE with machine learning models to address class imbalance, leading to more accurate and reliable classification outcomes. The study offers valuable insights for developing AI-based medical decision support systems focused on sleep disorder diagnosis.
Klasifikasi Ketidakhadiran di Tempat Kerja Menggunakan Metode Support Vector Machine Natasya Sumeisey; Titik Misriati; Imam Nawawi
Jurnal Komputer, Informasi dan Teknologi Vol. 5 No. 1 (2025): Juni
Publisher : Penerbit Jurnal Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53697/jkomitek.v5i1.2618

Abstract

Penelitian ini bertujuan untuk mengklasifikasikan ketidakhadiran karyawan di tempat kerja menggunakan metode Support Vector Machine (SVM), dengan menggunakan dataset yang mencakup informasi demografi, pekerjaan, dan faktor-faktor lain yang terkait dengan ketidakhadiran. Dataset yang digunakan berisi catatan ketidakhadiran karyawan sebuah perusahaan kurir di Brasil dari UCI Machine Learning. Penelitian ini mengimplementasikan model SVM dengan kernel Radial Basis Function (RBF), yang dipilih karena kemampuannya dalam menangani data non-linier. Hasil evaluasi model menunjukkan kinerja yang sangat baik, dengan AUC sebesar 0,995, akurasi mencapai 98,1%, dan skor F1 sebesar 0,981, yang menunjukkan keseimbangan yang sangat baik antara presisi dan ingatan. Model tersebut berhasil memprediksi sebagian besar ketidakhadiran karyawan secara akurat, dengan kesalahan prediksi yang minimal. Namun, masih ada beberapa kesalahan kecil dalam memprediksi ketidakhadiran, yang dapat diperbaiki dengan menyetel hiperparameter dan menambahkan fitur tambahan yang terkait dengan faktor-faktor yang memengaruhi ketidakhadiran. Secara keseluruhan, penelitian ini menunjukkan bahwa model SVM merupakan alat yang efektif dan efisien untuk memprediksi ketidakhadiran karyawan, dengan hasil yang dapat diterapkan dalam mengelola ketidakhadiran di organisasi.
Explainable Machine Learning for Multi-Class Classification of Internet Firewall Traffic Titik Misriati; Riska Aryanti
Bulletin of Informatics and Data Science Vol 5, No 1 (2026): May 2026
Publisher : PDSI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61944/bids.v5i1.163

Abstract

The increasing diversity and scale of network traffic introduce significant challenges in performing accurate and interpretable firewall analysis. This research aims to bridge the gap between predictive performance and model transparency by developing an explainable machine learning framework for multi-class firewall traffic classification. The study utilizes the Internet Firewall Data dataset consisting of 65,532 network traffic instances distributed across four firewall action classes and evaluates seven classification algorithms, including Decision Tree, Random Forest, XGBoost, Support Vector Machine, k-Nearest Neighbors, Naïve Bayes, and Logistic Regression. The dataset was partitioned using a stratified 80:20 hold-out approach to preserve the original class distribution and the experimental process involves data preprocessing, normalization, and validation on an independent test set using accuracy, precision, recall, and F1-score metrics. The findings reveal that XGBoost achieves the highest performance, reaching an accuracy of 99.81%, followed by Decision Tree and Random Forest. This indicates that ensemble and tree-based approaches are highly effective in modeling complex and non-linear traffic patterns. To improve interpretability, this study incorporates explainable artificial intelligence techniques, including feature importance and SHAP analysis. The results show that traffic-related attributes significantly influence classification outcomes, providing meaningful insights into firewall decision behavior
Optimization of Crop Recommendation Model Using Ensemble Learning Techniques for Multiclass Classification Siti Marlina; Titik Misriati; Riska Aryanti
Computer Science (CO-SCIENCE) Vol. 6 No. 1 (2026): January 2026
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/co-science.v6i1.10044

Abstract

Crop recommendation systems play a crucial role in modern agriculture by helping farmers make data-driven decisions to maximize yield, optimize resource use, and ensure sustainable farming practices. By analyzing environmental and soil parameters, these systems can suggest the most suitable crops for specific conditions, reducing the risks of crop failure and improving overall productivity. This study evaluates the performance of five ensemble learning algorithms—Random Forest, Extra Trees, CatBoost, XGBoost, and LightGBM—for multiclass classification in a crop recommendation system. All models achieved high accuracy above 98%, with Random Forest demonstrating the best and most stable performance. The feature importance analysis revealed that climatic factors, particularly rainfall and humidity, contributed the most to prediction outcomes, followed by macronutrients such as potassium, phosphorus, and nitrogen. In contrast, temperature and soil pH showed relatively lower influence. These findings highlight the dominance of climatic factors over soil chemical properties and demonstrate the capability of ensemble learning methods to capture complex data patterns. Random Forest is recommended as the primary model to support more effective land management and crop cultivation strategies.
Soft Voting Based Optimized Ensemble for Migraine Type Classification Titik Misriati; Riska Aryanti; Henny Leidiyana
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 6 No. 3 (2025): Volume 6 Number 3 September 2025
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jatika.v6i3.861

Abstract

The accurate classification of migraine subtypes is a complex challenge in neurology, hindered by symptomatic similarities between types. This complexity necessitates advanced computational tools to support diagnostic precision. This study aims to develop and evaluate an optimized soft voting ensemble classifier to automate this multi-class classification task effectively. The methodology involved training eight base models—including Neural Network, Random Forest, and Gradient Boosting—on a publicly available migraine dataset, with an 80-20 train-test split. The top three performers were integrated into a soft voting ensemble, which aggregates their predicted probabilities to enhance decision robustness. Model performance was rigorously assessed using accuracy, precision, recall, F1-score, and AUC-ROC metrics. The results demonstrated that the proposed ensemble achieved superior performance, with an accuracy of 91.67% and an F1-score of 91.50%, outperforming all constituent models. Furthermore, the ensemble attained near-perfect AUC-ROC values across multiple classes, confirming its strong discriminatory capability. The study concludes that the soft voting ensemble is a highly effective and reliable approach for migraine subtype classification, offering significant potential as a decision-support tool in clinical environments. Future work will focus on hyperparameter optimization, explainability, and validation with larger multi-centric datasets to facilitate clinical adoption.
Optimalisasi Rasio Data pada K-Nearest Neighbor untuk Klasifikasi Multikelas Tingkat Obesitas Populasi Dewasa Dini Aprilia Langnegara; Titik Misriati; Imam Nawawi
Jurnal Sains dan Teknologi Informasi Vol 5 No 2 (2026): Maret 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jussi.v5i2.9853

Abstract

Obesity is a complex health issue that needs a strategy for assessing its severity to facilitate earlier recognition. One can determine an individual's obesity classification by analyzing their dietary habits, level of physical activity, and overall health status. This research aims to ascertain the K-Nearest Neighbor (KNN) algorithm's efficacy in accurately classifying seven various phases of obesity. The dataset employed for predicting obesity consisted of 2,111 samples drawn from a population of both genders. For KNN testing, the dataset was divided into training and test data, with the test data allocated over three separate scenarios, including varying ratios. The ratios of 70:30, 80:20, and 90:10 were utilized in these circumstances, respectively. The value of k was varied from k=2 to k=10. The optimal configuration was achieved with a 90:10 data split ratio and a k value of 2, as evidenced by the test results. This setup concurrently attained an accuracy of 90.05%, a precision of 90.56%, a recall of 89.80%, and an F1 score of 90.18%. This categorization error was most prominent when comparing the Normal Weight category to the Class I Overweight group. A properly preprocessed KNN algorithm can attain competitive accuracy over 90 percent in classifying population obesity levels, as demonstrated by this study's findings.
Combination of Criteria Importance Through Intercriteria Dependence and Simple Additive Weighting Methods for Multi-Criteria Barista Selection: A Decision Support Approach Verra Sofica; Titik Misriati
Journal of Decision Support Systems and Multi-Criteria Decision Making Vol. 1 No. 1 (2026): November 2026
Publisher : Asosiasi Peneliti Informatika dan Komputer untuk Riset (PILAR)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67449/jodesma.v1i1.3

Abstract

Barista selection is an important process in the coffee shop and hospitality industry because baristas are required to possess not only technical skills but also coffee knowledge, communication, work speed, accuracy, and creativity. Evaluating candidates based on a single criterion can result in subjective and less representative decisions. Therefore, this study aims to develop a Decision Support System (DSS) for barista candidate selection by integrating the Criteria Importance Through Intercriteria Dependence (CRITID) and Simple Additive Weighting (SAW) methods. CRITID is applied to determine objective criterion weights by considering data variation and inter-criteria relationships, while SAW is used to calculate preference values and rank the candidates. The results show that Work Speed (CB-06) has the highest criterion weight of 0.1311, followed by Technical Skills (CB-02) with 0.1275 and Coffee Knowledge (CB-04) with 0.1253. The SAW ranking identifies A7 as the highest-ranked candidate with a preference value of 0.9682, followed by A3 with 0.9603 and A8 with 0.8994. Sensitivity analysis involving 32 scenarios with criterion weight changes of ±0.05 and ±0.10 indicates that the ranking structure is relatively stable, with A7 and A3 consistently maintaining the first and second positions. These findings demonstrate that the integration of CRITID and SAW can support a more objective, systematic, measurable, and stable decision-making process for barista candidate selection.
Co-Authors Agustiani, Sarifah Aldian Mauluda Amanda Subhi Pertiwi Arie Kurniawan Arifin, Yosep Tajul Asriyani Sagiyanto ASRIYANI SAGIYANTO, ASRIYANI Dini Aprilia Langnegara Eka Dyah Setyaningsih Fachri, Muhamad Fachry Abda El Rahman Handayani Meilisa Hari Sugiarto - UBSI Haryani Henny Leidiyana Ida Zuniarti Ida Zuniarti, Ida Imam Nawawi Imam Nawawi Instianti Elyana Instianti Elyana Istiqomah, Amelia Maria, Vivi Martenia, Rina Mohammad Afdhol Rizal Muhammad Arif Sulistiyo Nadhifah, Rifda Nadhifah, Rifda Natasya Sumeisey Norma Yunita Nurvi Oktiani Oktaviyani Oktaviyani Rahayu Ningsih Rahayu Ningsih Rahmat Hidayat Rahmat Hidayat Rahmawati Rahmawati Ramadhani, Arya Rina Martenia Riska Aryanti SALMAN ALFARIZI Sari Hartini, Sari Siti Khotimatul Wildah Siti Marlina Siti Marlina, Siti Siti Nurajizah Sri Ariyati Verra Sofica Widiarina . Widiarina, Widiarina Wina Yusnaeni Yoseph Tajul Arifin