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All Journal PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Format : Jurnal Imiah Teknik Informatika Jurnal Abdimas BSI: Jurnal Pengabdian Kepada Masyarakat Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta Bina Insani ICT Journal Informatics for Educators and Professional : Journal of Informatics JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Komtika (Komputasi dan Informatika) JSiI (Jurnal Sistem Informasi) JOURNAL INFORMATICS, SCIENCE & TECHNOLOGY INTI Nusa Mandiri JIKA (Jurnal Informatika) Jurnal Sistem Informasi Bulletin of Computer Science Research Journal of Students‘ Research in Computer Science (JSRCS) Jurnal Pengabdian Kepada Masyarakat PROSISKO : Jurnal Pengembangan Riset dan observasi Rekayasa Sistem Komputer Socius: Social Sciences Research Journal Journal of Informatics and Information Security Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Komtika (Komputasi dan Informatika)
Henny Leidiyana
Universitas Bina Sarana Informatika
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Prediksi Limit Kredit Menggunakan Metode Regresi Linear Yusuf, Ahmad; Leidiyana, Henny; Budiawan, Imam
Socius: Jurnal Penelitian Ilmu-Ilmu Sosial Vol 3, No 2 (2025): September
Publisher : Penerbit Yayasan Daarul Huda Kruengmane

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.17212143

Abstract

Determining appropriate credit limits is essential for financial institutions to manage credit risk effectively while optimizing revenue. This study aims to develop a predictive model for credit limits using linear regression, incorporating primary features such as Rating, Income, and Balance. The dataset consists of 400 credit card customer records with 11 variables, comprising both numerical and categorical data. The research follows the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology, covering stages including business understanding, data understanding, data preparation, modeling, evaluation, and deployment. Data analysis was conducted using Google Colab, involving quality assessment, categorical feature encoding through label encoding, and data normalization utilizing MinMaxScaler. Correlation analysis results indicated that Rating, Income, and Balance have strong correlations with Credit Limit, hence these three variables were chosen as primary predictors for the modeling process. 
Aplikasi Parkir Berlangganan Untuk Karyawan Tenant pada Mal Summarecon Bekasi Meisyha, Rineke; Leidiyana, Henny
Bianglala Informatika Vol 11, No 2 (2023): Bianglala Informatika 2023
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/bi.v11i2.16889

Abstract

Abstrak  - Bertambahnya jumlah penduduk dan perkembangan ekonomi masyarakat, saat ini banyak yang memiliki kendaran sendiri. Di kota yang semakin padat, menemukan tempat parkir yang nyaman  bisa menjadi sulit, misalkan pada sebuah mal. Belum lagi pada akhir pekan atau libur menimbulkan antrian yang salah satu penyebabnya yaitu saat pembayaran. Pengguna fasilitas parkir bukan hanya pengunjung tetapi karyawan mal. Pada mal Sumarecon Bekasi, karyawan juga membayar parkir berlangganan dan mereka harus melaporkan transaksi perpanjangan parkir bulanan mereka. Biasanya hal tersebut dilakukan disela jam istrirahat atau waktu lainnya. Karena keterbatasan waktu maka penelitian ini membahas tentang perancangan aplikasi parkir berlangganan parkir online pada mal Sumarecon Bekasi. Aplikasi ini dirancang dengan metode Air Terjun, menggunakan UML dan ERD sebagai metode perancangan system dan basis data. Pengujian antarmuka dilakukan  untuk calon pengguna prototype. Pengujian antarmuka ini dilakukan menggunakan 2 macam pengujian, yaitu pengujian Front-end dan pengujian Back-end. Dengan adanya aplikasi ini dapat memberikan solusi bagi para karyawan mal agar lebih hemat dan praktis dalam melaporkan transaksi pembayaran berlangganan parkir yang selama ini dilakukan secara manual.Kata kunci: Mal Summarecon, Parkir Berlangganan, Metode Air Terjun Abstract  - With the increasing population and economic development of the community, many now have their own vehicles. In an increasingly crowded city, finding a convenient parking space can be difficult, say in a mall. Not to mention that on weekends or holidays it creates queues, one of the reasons for this is the time of payment. Users of parking facilities are not only visitors but mall employees. At Summarecon Bekasi mall, employees also pay for subscription parking and they must report their monthly parking renewal transactions. Usually this is done during breaks or other times. Due to time constraints, this research discusses the design of an online parking subscription application at the Summarecon Bekasi mall. This application is designed using the Waterfall method, using UML and ERD as a system and database design method. Interface testing is carried out for prospective prototype users. Interface testing is carried out using 2 types of testing, namely Front-end testing and Back-end testing. With this application, it can provide a solution for mall employees to be more economical and practical in reporting parking subscription payment transactions which have been done manually.Keywords: Summarecon Mall, subscription parking, Waterfall method
Soft Voting Based Optimized Ensemble for Migraine Type Classification Misriati, Titik; Aryanti, Riska; Leidiyana, Henny
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.
Sistem Informasi Fasilitas di DKI Jakarta berbasis Android dengan Algoritma Floyd Warshall Rachman, Ali; Leidiyana, Henny
Jurnal Komtika (Komputasi dan Informatika) Vol 4 No 1 (2020)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v4i1.3700

Abstract

Mobile devices, especially the Android operating system, are very easy to use to provide information related to work or location precisely and accurately, especially searching for the location of facilities in DKI Jakarta, such as hospitals, fire stations, restaurants, hotels, and other places. But so far the problem is often encountered related to inaccurate information reports where reports take the form of images and text without a real location statement. For this reason, it is necessary to design a mobile application that can provide detailed information about the location of several public facilities in DKI Jakarta using the Software Development Life Cycle (SDLC) method. Applications that are made can provide information that has several features including image information, title, description, location, and weather.
Sistem Pakar untuk Mendiagnosa Penyakit Persendian Menggunakan Metode Certainty Factor Leidiyana, Henny; Hariyanto, Risvan Dwi
Jurnal Komtika (Komputasi dan Informatika) Vol 4 No 1 (2020)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v4i1.3701

Abstract

If someone feels unwell, they will usually make a diagnosis and find the solutions before deciding to consult a doctor. As with joint disease with symptoms of pain that are still mild, there is no time to go to the doctor, fees, or other reasons. Especially now through information via the internet can be easily obtained. To assist in identifying and improving the accuracy of diagnosis, it is necessary to have a web-based expert system application to diagnose joint disease using certainty factor methods. The research method used is using SDLC (Software Development Life Cycle). An expert system that has been made can be used as early detection and get solutions for joint diseases and preventive measures to treatment
Aplikasi Pengendalian Persediaan Barang Berbasis Android dengan Metode Economic Order Quantity (EOQ) pada Bengkel Dunia Motor Leidiyana, Henny; Anugrah, Arya
Jurnal Komtika (Komputasi dan Informatika) Vol 4 No 2 (2020)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v4i2.4217

Abstract

Good inventory control is something that must be considered in inventory management. The world of motorbikes is a special Honda motorcycle workshop which has a problem, namely a large supply of slow moving parts while a shortage of fast moving supplies. Employees in the spare parts department are also often confused in picking up parts from the warehouse because of unclear inventory information. As a solution, an application is made that applies an optimal inventory of goods using Economic Order Quantity (EOQ), which is a method used to determine the most economical amount of purchases made every time a purchase. Using services from Firebase will simplify the application development process. Because the application is general in nature, for more specific use by considering the characteristics, type and size of the goods, the application can be developed.
Klasifikasi Sentimen Terhadap Kebijakan Tapera Menggunakan Komparasi Machine Learning dan SMOTE Leidiyana, Henny; Misriati, Titik; Aryanti, Riska
Jurnal Komtika (Komputasi dan Informatika) Vol 8 No 2 (2024)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v8i2.12595

Abstract

The Indonesian government's Public Housing Savings Program (Tapera) aims to help low- and middle-income persons get housing financing. Although the initiative strives to satisfy housing requirements, the public has responded in a variety of ways, as evidenced by social media posts. The goal of this study is to examine public sentiment towards the Tapera policy using YouTube comment data to provide an overview of popular perspective. This study combines sentiment analysis with machine learning algorithms, including K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Multinomial Naïve Bayes (NB), and Decision Tree. The data is divided into three scenarios, namely 70% training data and 30% test data, 80% training data and 20% test data, and 90% training data and 10% test data. Data balancing is also performed with SMOTE. The performance evaluation is based on each algorithm's accuracy, precision, recall, and F1 Score values. The results showed that the SVM algorithm performed the best in all circumstances, with the greatest accuracy of 88% and an F1 score of 85%. The multinomial Naïve Bayes algorithm ranked second with steady accuracy, whereas KNN and Decision Tree had poorer performance. This suggests that SVM is the most effective method for processing sentiment data regarding Tapera policy.
Pendekatan Hibrida Statistik dan Machine Learning untuk Peramalan Jumlah Kunjungan Turis Leidiyana, Henny; Nurajizah, Siti
Jurnal Komtika (Komputasi dan Informatika) Vol 9 No 2 (2025)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v9i2.14909

Abstract

Tourist arrival forecasting is a crucial aspect of planning and decision-making in the tourism sector. Accurate predictions are essential to anticipate surges or declines in visitor numbers, design effective marketing strategies, and manage resources efficiently. This study proposes a hybrid forecasting approach that integrates traditional statistical methods with machine learning algorithms to improve the accuracy of tourist arrival forecasts. Five forecasting models are implemented: ARIMA as a representative of traditional statistical models; Random Forest and Extreme Gradient Boosting (XGBoost) as machine learning models; a simple hybrid model, which combines ARIMA and XGBoost predictions through simple averaging; a weighted hybrid model, which merges the two models using performance-based weights; and a stacking hybrid model, which utilizes a meta-model to optimize prediction combinations. Given that the dataset exhibits significant pattern changes, or structural breaks, particularly during the COVID-19 pandemic, this study employs a rolling window backtesting approach for model evaluation. This method allows the models to be tested progressively across normal, crisis, and recovery periods, providing a realistic assessment of their performance under dynamic conditions. Model performance is evaluated using three key metrics: RMSE, MAE, dan MAPE. The results demonstrate that the stacking hybrid model consistently achieves the lowest RMSE across all test periods, highlighting its ability to capture complex trends and extreme fluctuations caused by COVID-19 Keywords: Rolling Window Backtesting, Weighted Hybrid, Weighted Hybrid.
Analisis Dampak SMOTE terhadap Feature Importance pada Klasifikasi Data Migraine menggunakan Random Forest dan Extra Trees Henny Leidiyana
Jurnal Komtika (Komputasi dan Informatika) Vol. 10 No. 1 (2026)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v10.i1.16684

Abstract

This study analyzes the impact of the Synthetic Minority Over-sampling Technique (SMOTE) on model performance and feature importance in the classification of migraine patients using Random Forest (RF) and Extra Trees (ET) algorithms. Evaluation was conducted based on recall and F1-Score for the minority class, as well as Permutation Importance analysis. The results indicate that ET, especially when combined with SMOTE (ET + SMOTE), delivers the best performance for the minority class. ET + SMOTE achieved an average F1-Score of 0.7000 and an average recall of 0.8041 using 11 optimal features, indicating better feature efficiency. The application of SMOTE significantly affected the ranking of important features. Although SMOTE improved detection for some minority classes, its impact was not always consistent and occasionally reduced performance on other minority classes. This study concludes that SMOTE alters feature contributions and model interpretability, as well as enhances performance on certain minority classes, particularly when combined with ET.
Co-Authors Agustiena Merdekawati, Agustiena Ahmad Yusuf Al Ghazali Ali Rachman, Ali Andrie Kurniawan Angga Aditya Permana Anugrah, Arya Deval Saladin Ridwan Dewi Ayu Nur Wulandari Fajar Santoso Farida Cinda R. S Simatupang Gustina, Rina Hariyanto, Risvan Dwi Hary Priantoro Imam Budiawan Indrawan Bagus Pertiaz Iqbal Fadjarudin Iskandar Yusuf Joniwarta Luci Kanti Rahayu Meisyha, Rineke Muhammad Adil Muhammad Faisal Muhammad Ghudafa Taufiq Akbar Nandang Iriadi Nila Hardi Omar Pahlevi Purnamawati Purnamawati Rio Sudarsono Riska Aryanti Simanungkalit, Jumantho Simanungkalit, Jumantho Siti Nurajizah Syifa Nur Rakhmah, Syifa Nur Titik Misriati Waryono Yopi Handrianto, Yopi Yoraeni, Ani