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All Journal Media Statistika JURNAL SISTEM INFORMASI BISNIS Telematika : Jurnal Informatika dan Teknologi Informasi Jurnal Teknologi Informasi dan Ilmu Komputer Seminar Nasional Informatika (SEMNASIF) JOURNAL OF APPLIED INFORMATICS AND COMPUTING PINTER : Jurnal Pendidikan Teknik Informatika dan Komputer JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) bit-Tech Jurnal Sistem Informasi dan Informatika (SIMIKA) Jurnal Informasi dan Teknologi JATI (Jurnal Mahasiswa Teknik Informatika) G-Tech : Jurnal Teknologi Terapan International Journal of Advances in Data and Information Systems ESTIMASI: Journal of Statistics and Its Application Jurnal Statistika dan Matematika (Statmat) Journal of Advanced in Information and Industrial Technology (JAIIT) Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Nusantara Science and Technology Proceedings Jurnal Teknik Informatika (JUTIF) HOAQ (High Education of Organization Archive Quality) : Jurnal Teknologi Informasi Journal of Technology and Informatics (JoTI) International Journal of Data Science, Engineering, and Analytics (IJDASEA) International Journal Of Computer, Network Security and Information System (IJCONSIST) Journal of Information Systems and Technology Research Journal of International Conference Proceedings Jurnal Teknik Terapan (J-TETA) Journal of Data Mining and Information Systems Parameter: Jurnal Matematika, Statistika dan Terapannya Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal PETISI (Pendidikan Teknologi Informasi) Joong-Ki Jurnal Pengabdian Masyarakat SENSASI
Kartika Maulida Hindrayani
Universitas Pembangunan Nasional Veteran Jawa Timur
Religion Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Social Sciences Other

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ANALISIS PERBANDINGAN ALGORITMA APRIORI DAN FP-GROWTH DALAM MENENTUKAN POLA PEMBELIAN KONSUMEN TOKO BANGUNAN Muhimmatul Arofah; Mohammad Idhom; Kartika Maulida Hindrayani
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 9 No. 1 (2026): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/v9bepx08

Abstract

Micro, Small, and Medium Enterprises (MSMEs) are vital to Indonesia's economy but often face challenges in inventory control and understanding consumer behavior. This study aims to compare the performance of the Apriori and FP-Growth algorithms in identifying consumer purchasing patterns from 7,778 transaction records at UD. Kurnia, a building material store, between August 2023 and July 2024. Unlike previous research that relied only on support and confidence metrics, this study applies the lift metric, which measures the strength of item associations, to minimize misleading rules. The algorithms were tested under 15 combinations of minimum support and lift threshold values. Results show that both algorithms generate the same association rules, but Apriori is significantly faster. At a minimum support of 0.0005 and a lift threshold of 1.5, Apriori completes processing in 3.23 seconds, while FP-Growth takes 21.81 seconds. With these findings, store owners can make more precise inventory decisions and implement data-driven cross-selling strategies, such as offering semen gresik when colt pasir is purchased.
Application of Multivariate Singular Spectrum Analysis for Weather Prediction Abdul Mukti; Kartika Maulida Hindrayani; Mohammad Idhom
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3003

Abstract

Weather significantly influences various aspects of life, especially in urban areas like Surabaya, where unpredictable weather can disrupt transportation, public health, economic activities, and overall comfort. Among the key meteorological variables, air temperature and relative humidity are crucial for assessing human thermal comfort, as their interaction forms the heat index a key indicator of health risks in tropical regions. This study introduces the use of the Multivariate Singular Spectrum Analysis (MSSA) method to forecast daily weather parameters, including minimum temperature (TN), maximum temperature (TX), average temperature (TAVG), and average relative humidity (RH_AVG). The research utilized weather data from the Perak 1 Meteorological Station in Surabaya, spanning from August 1 to December 31, 2024 (training data) and January 1 to January 14, 2025 (testing data). Unlike traditional methods, the MSSA model effectively analyzes the complex relationships between multiple weather variables, improving forecasting accuracy. The model demonstrated strong performance, with Mean Absolute Percentage Errors (MAPE) of 3.70% for TN, 5.99% for TX, 4.44% for TAVG, and 7.39% for RH_AVG. These results highlight MSSA's potential as an effective tool for short-term weather forecasting in urban tropical environments, supporting more accurate predictions that can inform early warning systems, disaster planning, and public health strategies. This work advances the state-of-the-art by offering a robust method for handling multivariate weather data, which is essential for making informed decisions in rapidly changing climates
Effectiveness of Extreme Learning Machine in Online Payment Transaction Fraud Detection Radya Ardi; Mohammad Idhom; Kartika Maulida Hindrayani
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3005

Abstract

The rise of fintech and digital payment systems has increased efficiency but also escalated the risk of online transaction fraud, particularly under imbalanced data conditions where fraudulent cases are rare. This study addresses the limitations of traditional rule-based and machine learning models in such scenarios by proposing the use of Extreme Learning Machine (ELM) with hyperparameter tuning as a novel and efficient solution for fraud detection. Unlike most prior studies relying on default settings or data resampling, this research focuses on enhancing ELM performance purely through parameter optimization using the Optuna framework. A dataset of 20,000 real-world online transactions was used to evaluate model performance before and after tuning. In its default configuration, ELM yielded high overall accuracy (96.80%) but failed to detect fraudulent cases (0% recall and F1-score). After tuning key parameters such as the number of hidden neurons and activation function, the model achieved a significantly better balance between accuracy and fraud detection performance, with 99.53% accuracy, 98.20% precision, 86.51% recall, and a 91.98% F1-score. These results demonstrate that hyperparameter tuning alone, without resampling, can substantially improve ELM’s sensitivity to minority class detection. The findings suggest that optimized ELM offers a promising alternative for real-time fraud detection in imbalanced financial datasets, contributing to more adaptive and reliable security systems in the digital finance landscape.
ARIMA-TGARCH Model for Return Prediction and Risk Estimation with VaR Imanta Ginting; Trimono Trimono; Kartika Maulida Hindrayani
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3090

Abstract

Investment activity in the Indonesian capital market has experienced significant growth, driven by increasing public awareness and accessibility to financial instruments. Stocks remain the most favored investment tool due to their potential for high returns, though they come with higher risks. Accurate modeling of return dynamics and risk estimation is thus crucial for informed investment decisions. This study analyzes the return and volatility of PT Telekomunikasi Indonesia Tbk (TLKM) stock using a hybrid time series approach that combines the Autoregressive Integrated Moving Average (ARIMA) model and the Threshold Generalized Autoregressive Conditional Heteroskedasticity (TGARCH) model. The analysis uses daily closing price data from 2020 to 2024, with 1,210 observations. The best-fitting model, ARIMA(2,0,2)–TGARCH(1,1), resulted in low Root Mean Squared Error (RMSE) values of 0.0188 for both training and testing datasets, indicating strong prediction accuracy. Forecasting over a five-day horizon revealed fluctuating returns and a decreasing trend in volatility, from 0.0230 to 0.0198. Additionally, the study utilized the Value at Risk (VaR) method to estimate potential losses under normal market conditions. At a 95% confidence level, the predicted daily loss for a capital investment of IDR 50,000,000 ranged between IDR 1,633,108 and IDR 1,859,355. The combination of ARIMA and TGARCH, integrated with VaR, provides a comprehensive framework for capturing both linear return trends and asymmetric volatility, offering investors a robust quantitative tool for managing risks and optimizing strategies.
Heckman Probit Two-Step Regression Approach for Analyzing Open Unemployment Factors in West Java Province Holly Patrycia; Dwi Arman Prasetya; Kartika Maulida Hindrayani
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3096

Abstract

Open unemployment remains a major socio-economic challenge in Indonesia, with West Java recording the highest national rate in August 2024 at 6.75%. This study investigates the determinants of open unemployment using the Heckman Probit Two-Step model, an approach rarely applied in Indonesian labor market research. Unlike conventional regression methods, this model corrects for sample selection bias by simultaneously estimating labor force participation and unemployment status. Data are drawn from the 2024 Survei Angkatan Kerja Nasional (SAKERNAS) conducted by Badan Pusat Statistik (BPS), covering working-age individuals in West Java Province. The first stage models labor force entry, while the second stage incorporates the Inverse Mills Ratio (IMR) to adjust for selection effects. Results show that the IMR coefficient (–0.3100, p = 0.0412) is statistically significant, confirming the necessity of the two-step correction. The explanatory power of the model is substantial, with Pseudo-R² values of 0.385 for labor force participation and 0.381 for open unemployment. Marginal effects indicate that being married reduces unemployment probability by 5.50%, each additional year of age decreases it by 2.79%, whereas a longer job search increases it by 3.35%. Training experience lowers unemployment risk, while disabilities and larger household size increase vulnerability. Methodologically, the study demonstrates the advantages of Heckprobit in producing unbiased estimates compared to descriptive or conventional probit approaches previously used in Indonesia. Nonetheless, the cross-sectional design and focus on a single province limit generalizability. Findings provide valuable evidence for policymakers to design targeted, inclusive employment strategies aligned with regional development goals
Forecasting Financial Sector Stock Price and Loss Risk Using the ARIMAX and Value-at-Risk Methods Amanda Aulia; Trimono Trimono; Kartika Maulida Hindrayani
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3219

Abstract

Stock price volatility remains a persistent challenge in financial forecasting, as traditional ARIMA-based models often neglect the role of macroeconomic forces, leading to limited predictive robustness. Addressing this methodological gap, this study uniquely integrates the Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) model and the Value-at-Risk (VaR) framework to simultaneously predict stock prices and quantify investment risk. This dual approach advances prior forecasting literature by merging predictive modeling and risk assessment within a single analytical structure. Using daily data from PT Bank Central Asia Tbk (BBCA) and the USD/IDR and SGD/IDR exchange rates from January 2019 to September 2024, model identification through ACF, PACF, and the Akaike Information Criterion (AIC) identifies ARIMAX(0,1,1) as optimal. The model achieves a Mean Absolute Percentage Error (MAPE) of 2.19%, indicating very high predictive accuracy. Although forecasted movements appear smoother than observed fluctuations, the model effectively captures short-term market trends influenced by exchange rate dynamics. Historical simulation at a 95% confidence level estimates a daily Value-at-Risk (VaR) of 1.71%, implying a potential loss of approximately Rp17,144 per Rp1,000,000 invested. These results demonstrate that integrating ARIMAX with VaR not only enhances statistical precision but also provides practical value for investors and policymakers. The combined framework enables evidence-based decision-making, portfolio optimization, and risk mitigation in volatile capital markets, offering a replicable and data-driven model for financial forecasting under macroeconomic uncertainty.
Intelligent Detection of Spermatozoa Motility Using YOLOv5: Toward Efficient and Accurate Male Fertility Analysis Christina Halim; Wahyu Syaifullah JS; Kartika Maulida Hindrayani; I Gede Susrama Mas Diayasa
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3231

Abstract

Detecting multiple spermatozoa in microscopic videos remains a complex challenge due to their small size, high velocity, frequent overlap, and inconsistent illumination. This study introduces an enhanced real-time detection framework using the YOLOv5 deep learning algorithm, representing a significant advancement over previous Computer-Assisted Sperm Analysis (CASA) systems that primarily relied on classical image processing or earlier YOLO versions (e.g., YOLOv3, YOLOv4). Unlike these predecessors, the proposed YOLOv5-based model integrates Cross Stage Partial (CSP) architecture and optimized feature pyramid networks, allowing for superior detection of small, fast-moving spermatozoa with reduced computational complexity and model size. A curated dataset of sperm motility videos was processed through standardized steps—frame extraction, contrast enhancement, and manual annotation—to ensure uniformity and data quality. The model, trained via transfer learning on images of 640×640 pixels over 50 epochs, achieved a precision of 0.6333, recall of 0.627, and mAP@0.5 of 0.618, while maintaining real-time performance at 93 frames per second (FPS). Compared to YOLOv4, the proposed framework reduced training time by two-thirds (from 3 hours to 1 hour) and decreased model size from 244 MB to 13.8 MB, without compromising accuracy. These improvements establish YOLOv5 as a lightweight and scalable AI model for sperm detection, enabling automated, objective, and reproducible motility assessment. Clinically, this approach enhances the precision and consistency of male fertility diagnostics, paving the way toward AI-driven reproductive health evaluation and more accessible fertility screening solutions in both advanced and resource-limited laboratory settings.
OPTIMASI PUSAT CLUSTER K-PROTOTYPES PADA PENGELOMPOKAN PENERIMAAN BANTUAN REHABILITASI RUTILAHU DI KOTA SURABAYA Ningrum, Lisya Septyo; Hindrayani, Kartika Maulida; Jauharis Saputra, Wahyu Syaifullah
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 11, No 1 (2026)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v11i1.7656

Abstract

Clustering merupakan teknik penting dalam data mining yang digunakan untuk mengelompokkan data berdasarkan kesamaan karak-teristik. Algoritma K-Prototypes sering digunakan pada data bertipe campuran karena mengombinasikan K-Means untuk atribut numerik dan K-Modes untuk atribut kategorikal. Namun, kinerjanya sangat ber-gantung pada inisialisasi pusat klaster awal. Penelitian ini men-gusulkan penerapan tiga algoritma optimasi yaitu Particle Swarm Op-timization (PSO), Genetic Algorithm (GA), dan Flower Pollination Algorithm (FPA) untuk meningkatkan performa K-Prototypes dalam pengelompokan calon penerima program rehabilitasi Rumah Tidak Layak Huni (Rutilahu) di Kota Surabaya. Evaluasi dilakukan menggunakan Davies-Bouldin Index (DBI), Silhouette Score, dan wak-tu komputasi. Berdasarkan hasil penelitian menunjukkan bahwa PSO memberikan hasil terbaik dengan DBI terendah sebesar 0,6467, Silhou-ette Score tertinggi sebesar 0,5498, dan waktu komputasi tercepat yaitu 23,5168 detik. GA menghasilkan DBI tertinggi sebesar 0,7134, Silhou-ette Score sebesar 0,5143, serta waktu komputasi terlama yaitu 7220,6384 detik. FPA memiliki DBI 0,6467 dan Silhouette Score yang sama dengan PSO, tetapi dengan waktu komputasi sebesar 3415,9175 detik. Dengan demikian, PSO terbukti paling efektif dalam meningkat-kan akurasi dan efisiensi clustering K-Prototypes, serta mendukung distribusi bantuan yang lebih adil dan tepat sasaran.
Bayesian Spatio-Temporal Conditional Autoregressive Modeling of Stunting Risk Factors in East Java Ardia Eva Ardiani; Trimono; Kartika Maulida Hindrayani
Journal of Advances in Information and Industrial Technology Vol. 8 No. 1 (2026): May
Publisher : LPPM Telkom University Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52435/jaiit.v8i1.762

Abstract

This study analyzes stunting cases in East Java Province using district/city-level panel data covering the period 2022-2024. The data were obtained from the Indonesian Nutritional Status Survey (SSGI), the Indonesian Health Survey (SKI), and official statistical sources, consisting of stunting cases and several health, socioeconomic, and environmental indicators across 38 districts and municipalities. The study applies a Bayesian Spatio-Temporal Conditional Autoregressive (BST-CAR) model with the Integrated Nested Laplace Approximation (INLA) approach to account for spatial dependence among neighboring regions and temporal variation over time. The results show that stunting cases in East Java exhibit significant spatial and temporal dependence, supported by significant positive spatial autocorrelation across all observation years. Model evaluation yields a Deviance Information Criterion (DIC) value of 1477,267 and a Watanabe-Akaike Information Criterion (WAIC) value of 1442,479. The estimation results indicate that all examined covariates, including low birth weight, complete basic immunization, exclusive breastfeeding, proportion of poor population, access to improved drinking water, and access to improved sanitation, are statistically significant in explaining variations in stunting cases after controlling for spatial and temporal effects. Relative risk mapping reveals clear spatial heterogeneity, with higher-risk clusters concentrated in districts such as Jember, Lumajang, and Probolinggo, while lower-risk areas are mainly observed in urban regions such as Surabaya, Mojokerto, and Madiun. Overall, the findings suggest that stunting distribution in East Java is shaped by both spatial and temporal structures, highlighting the importance of geographically targeted intervention strategies at the district/city level.
Sentiment Analysis on Generation Z News Article using Support Vectore Machine (SVM) with Synthetic Minority Over-sampling Technique (SMOTE) Kartini Kartini; Kartika Maulida Hindrayani; Betty Dewi Puspasari
IJCONSIST JOURNALS Vol 5 No 2 (2024): March
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v5i2.141

Abstract

The development of digital media has increased the volume of news articles discussing various issues, including those involving Generation Z. Understanding public perception of these news items can be achieved by applying a crucial approach, namely sentiment analysis. This study aims to classify sentiment in news articles about Generation Z using the Support Vector Machine (SVM) algorithm. The main challenge in sentiment analysis is data class imbalance, where the amount of positive and negative sentiment data is often unbalanced. Therefore, the Synthetic Minority Over-sampling Technique (SMOTE) is used to address this problem by balancing the class distribution before model training. The datasets used were collected from various online news portals and analyzed through text preprocessing, feature extraction using Bag of Word, and SVM model training. The evaluation results show that the application of SMOTE significantly improves the model's performance in classifying sentiment, with improvements in accuracy, precision, recall, and F1-score compared to the model without data imbalance handling. This study demonstrates that the combination of SVM and SMOTE is effective in conducting sentiment analysis on Generation Z news articles. The accuracy shows 84% with 83% precision and 76% recall.
Co-Authors Aang Kisnu Darmawan Abdul Mukti Achmad Dzulfiqar Alfiansyah Adhigiadany, Chelsea Ayu Afidria, Zulfa Febi Ahmad, Davin Anezta Aisyah Kirana Putri Isyanto Aji R, Prismahardi Altetiko, Faizal Johan Alya Mirza Safira Alzam, Muhammad Arsyad Amanda Aulia Amelia, Meisya Vira Amri Muhaimin Ardia Eva Ardiani Arkananta Handoyo Aulia Nur Fitriani Aviolla Terza Damaliana Azizah Zalfa Assyadida Azizah, Alisa Jihan Betty Dewi Puspasari Bhalqis, Anissa Andiar Brescia Ayundina Yuniarossy Budi, Aditya Septa Burhan Syarif Acarya Chelsea Ayu Adhigiadany Christina Halim Christina, Enzelica Vica Damaliana, Aviolla Terza Diyasa, I Gede Susrama Mas Dwi Arman Prasetya Dwi Arman Prasetya Dwi Arman Prasetya Dwi Arman Prasetya Edelin Fortuna Elmaliyasari, Shifa Endang Tri Wahyurini Fahrudin, Tresna Maulana Fajar Ramadhani Fajria Ulumin Nafiah Fernando, Moch. Firman Hilya Zada Mardhatilla Al Haadiy Holly Patrycia I Gede Susrama Mas Diayasa idhom, Mohammad Imam Imanta Ginting Imelda Widya Ningrum Indira Zein Rizqin Isyanto, Aisyah Kirana Putri Kartini Kartini Kartini Kartini Khairunisa, Adenda Kristananda, Raja Valentino Lidya Musaffak, Awal Made Hanindia Prami Swari Maudi Adella Maulana F, Tresna Meisya Vira Amelia Meisya Vira Amelia Milla Akbarany Baktiar Putri Mohammad Idhom Mohammad Idhom Mohammad Idhom Muhammad Rafli Muhimmatul Arofah Nanda Kurnia Wardati Ni Luh Ayu Nariswari Dewi Ningrum, Imelda Widya Ningrum, Lisya Septyo Nur Aini Rakhmawati Pakpahan, Vera Febrianti Pratiwi, Nanda Aulia Prismahardi Aji Riyantoko Purwadwika, Reza Sadiya Putro, R. Kokoh H. rachmanto, Nugroho Fajar Radya Ardi Renaldy Al Ikhsan Reza Sadiya Purwadwika Rhomaningtias, Lina Riskiyah, Ameliyah Risnaldy Novendra Irawan Rizky Fatkhur Rohman Safira, Alya Mirza Safitri, Eristya Maya Saputra, Wahyu S. J. Selena Nurmanina Afandy Selly Rizkiyah Shindi Shella May Wara Shindi Shella May Wara Shindi Shella May Wara Sinthya Putri, Diana Steffany Marcellia Witanto Thoriqulhaq, Muhammad Tresna Maulana F Tresna Maulana Fahruddin Tresna Maulana Fahrudin Tresna Maulana Fahrudin Trimono Trimono Trimono Trimono Trimono Trimono Trimono Trimono Trimono, Trimono Wahyu Syaifullah Jauharis Saputra Wahyu Syaifullah JS Wibowo, Muhammad Bagas Satrio Yosua Satria Bara Harmoni