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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 Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Komtika (Komputasi dan Informatika)
Henny Leidiyana
Universitas Bina Sarana Informatika
Religion Arts Humanities Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Nursing Public Health Social Sciences Veterinary Other

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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.
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 Iqbal Fadjarudin 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