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All Journal Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Creative Information Technology Journal Information System Journal (INFOS) Intellect : Indonesian Journal of Learning and Technological Innovation Fahma : Jurnal Informatika Komputer, Bisnis dan Manajemen
Donni Prabowo
Universitas AMIKOM Yogyakarta
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering

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Implementasi Konsep Gamification pada Aplikasi Terapi Autis dengan Metode Applied Behavior Analysis Donni Prabowo; Ema Utami; Hanif Al Fatta
Creative Information Technology Journal Vol 1, No 3 (2014): Mei - Juli
Publisher : UNIVERSITAS AMIKOM YOGYAKARTA

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (599.82 KB) | DOI: 10.24076/citec.2014v1i3.22

Abstract

Autisme merupakan gangguan yang dialami sejak lahir ataupun saat balita. Gangguan ini merupakan kelainan perkembangan sistem saraf pada seseorang. Penderita autisme umumnya mengalami kesulitan dengan fungsi sosial, motorik, sensorik, dan kognitif. Salah satu penanganan autisme yaitu dengan melakukan terapi. Metode terapi autis yang paling sering digunakan sampai saat ini yaitu metode Applied Behavior Analisyst (ABA). Saat ini penerapan konsep permainan atau gamification merupakan salah satu cara untuk membuat penderita autism antusias untuk melakukan terapi, oleh karena itu penelitian ini akan membahas perancangan aplikasi game terapi autis pada empat bidang yang menjadi masalah bagi penderita autism yaitu bidang sosial, motorik, sensorik, dan kognitif. Rancangan aplikasi game terapi ini akan diterapkan pada sistem terapi autis yang menerapkan metode ABA yaitu sistem Ahada (ahada.info). Pendekatan yang digunakan dalam proses perancangan game terapi pada penelitian ini adalah model Game Design Document menurut Adams Ernest. Penelitian ini menghasilkan kesimpulan yang menyatakan bahwa implementasi konsep game pada sistem terapi autis Ahada dapat diterapkan dengan menggunakan pendekatan Game Design Document. Pendekatan ini dinyatakan sebagai pendekatan yang baik untuk digunakan dalam proses perancangan game. Selain itu, penelitian ini juga menyatakan bahwa penerapan konsep Natural User Interface (NUI) dapat diintegrasikan dengan sistem Ahada untuk memenuhi kebutuhan terapi motorik.Autism is disorder experienced since the time of birth or infancy. This disorder is developmental disorder of the nervous system in person. People with autism generally have difficulty with social, motor, sensory, and cognitive. One of autism treatment is therapy. Autism treatment methods are most commonly used to date are Applied Behavior Analisyst method (ABA). Now, game concept application is one way to create enthusiasm for autism therapy, therefore this research will discuss about design of autism therapy game on four areas that become problem for people with autism are social, motor, sensory, and cognitive. The design of the therapy game application will be applied to autism therapy system that implements the method ABA, that is Ahada system. The approach used in the game design process of therapy in this study is Game Design Document by Ernest Adams. This study resulted conclusion that the concept of gaming on Ahada autism therapy system can be implemented by using Game Design Document. This approach is expressed as good approach to use in game design process. In addition, this study also stated that the application of the Natural User Interface concept can be integrated with the system to meet the needs motor therapy.
PENERAPAN ALGORITMA APRIORI UNTUK REKOMENDASI BUKU PADA AMIKOM RESOURCE CENTER Donni Prabowo; Fitri Ramdani
Information System Journal Vol. 3 No. 1 (2020): Information System Journal (INFOS)
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/infosjournal.2020v3i1.207

Abstract

Amikom Resource Center each time producing recorded data, the activity is carried out for years so that it makes Big Data. The big data has the opportunity to produce information that can be useful for borrower and library management.One of the data mining techniques that can be used is the Apriori algorithm with the association rules technique. By using book lending transaction data, apriori algorithm will form association rules between books which are then used to determine book recommendations. In addition, the results of apriori analysis can also be used by the library as information to findout which books are often borrowed, the placement of the book layout in the Amikom library.The results showed that the association rules formed from 562 book lending transaction data in November 2019 used a minimum book frequency of 4 or a minimum support value of 0.7% and a minimum confidence of 80% resulting in 10 association rules with all rules having a positive correlation so that it can be used as a reference for giving book recommendations.
PREDIKSI PEMBERIAN KELAYAKAN PINJAMAN DENGAN METODE FUZZY TSUKAMOTO Nurul Ajeng; Bety Wulan Sari; Donni Prabowo
Information System Journal Vol. 3 No. 1 (2020): Information System Journal (INFOS)
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/infosjournal.2020v3i1.215

Abstract

Sentra Gadai is a place to borrow in Yogyakarta. Every day giving loans to customers. In granting a loan the Senta Gadai has a condition that is a loan size of 50% of the collateral price. If the loan is more than 50%, the Sentra Gadai sometimes still hesitate to provide the loan. The Loan Eligibility Prediction System is used to help the Sentra Gadai in making decisions by providing alternative estimates in determining the feasibility of borrowing by the customer. This prediction system uses Tsukamoto's fuzzy method in estimating the feasibility of loans to customers by having several criteria such as the duration of the loan, the price of the guarantee and the condition of the goods. This prediction system is based on desktop because it is only used by the Sentra Gadai and not to the public with the Java programming language and database using phpMyAdmin. Keywords : Prediction System, Loan,Fuzzy Tsukamoto
PERBANDINGAN ALGORITMA NAÏVE BAYES DAN C4.5 DALAM MENENTUKAN TINGKAT PENJUALAN MOTOR HONDA Donni Prabowo; Firman Hidayat; Gagah Gumelar; Dewa Qintoro; Aji Setiawan
Jurnal Informatika Komputer, Bisnis dan Manajemen Vol 16 No 3 (2018): September 2018
Publisher : LPPM STMIK El Rahma Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61805/fahma.v16i3.91

Abstract

At this time, the mobility of the community activities will be very high. The mobility of the high society will have an impact to the transportation needs are increasing. Coupled with the opportunities of the community towards taxi online, will have an impact on the number of sales of motorcycles in Indonesia. In this research we will classify the sales data of the motor with rapidminer software by applying two algorithm: Algorithm C45, and Naive Bayes. The second algorithm is a method of classification based on statistics and probability. Two of the above algorithms compared the rate of speed in the best-selling motorcycle classification of data, to get the decision tree the best selling motorcycle was purchased by the society, and should be in more production. Research using Algorithm C45 motor with the lowest prices is the best selling, than Algorithm Naïve Bayes where the best selling honda motor is a type of Scooter, medium of CC, and low price.
Implementation of the Levenshtein Distance Algorithm and the Regular Search Expression Method for Detecting Typors in Javascript Mu’alif Lihawa; Anggit Dwi Hartanto; Norhikmah Norhikmah; Donni Prabowo; Ika Nur Fajri; Wiwi Widayani
Sistemasi: Jurnal Sistem Informasi Vol 12, No 2 (2023): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v12i2.2795

Abstract

Typing is an activity to write an article in printed form that has been assembled by a typewriter. With the rapid development of the times, typewriters were replaced by computers because they were efficient in making writing or text. a text or writing that is easy to understand in conveying information does not have word mistakes that result in unclear information being conveyed. In word processing applications such as Microsoft Office Word, it has the word suggestions and autocorrect word features which are very useful in checking an article where there are word errors in the writing. This research develops a javascript library to detect typo errors for writing wrong words and recommends the right words to change the wrong words. This study uses the Levenshtein Distance Algorithm and the Regular Search Expression method. The results of this study were successfully applied to the word recommendation feature in the library with an accuracy value of 50% and a precision level of 5%.
Stock Price Prediction Using SVR: A Feature Engineering and Hyperparameter Tuning Approach Alfian Ramadhan; Yoga Pristyanto; Anggit Dwi Hartanto; Donni Prabowo
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 3 (2026): June 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i3.7249

Abstract

Stock price prediction in Indonesia's volatile mining sector poses significant forecasting challenges driven by commodity price dynamics and structural market shifts. This study proposes a systematic prediction framework for PT Indo Tambangraya Megah Tbk (ITMG.JK) integrating technical and market-derived non-technical feature engineering, LightGBM-based feature selection, multilevel TimeSeriesSplit cross-validation, and hyperparameter optimization. Support Vector Regression (SVR) is benchmarked against LightGBM, XGBoost, and Random Forest under 5-fold, 10-fold, and 15-fold schemes. SVR achieves the best performance at 10-fold, with RMSE of 0.0121, MAE of 0.0090, MAPE of 1.1457%, and R² of 0.9249. Generalization experiments across four additional stocks in banking, automotive, and mining sectors confirm SVR's robustness, maintaining R² above 0.89 and MAPE below 2.65% in all cases while tree-based models produce negative R² on certain datasets. Statistical validation via Wilcoxon signed-rank test (p < 0.05) and Cohen's d (|d| > 0.8) confirms the significance of SVR's advantage. These findings indicate that SVR consistently outperforms the evaluated models under the proposed experimental framework.
Analisis Perbandingan Prediksi Harga Rumah Dengan Random Forest, Gradient Boosting, dan XGBoost Bety Wulan Sari; Donni Prabowo
Intellect : Indonesian Journal of Learning and Technological Innovation Vol. 4 No. 1 (2025): Intellect : Indonesian Journal of Learning and Technological Innovation
Publisher : Yayasan Lembaga Studi Makwa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57255/intellect.v4i1.1385

Abstract

House price prediction poses a significant challenge in the property sector, especially in the Yogyakarta region, which exhibits a wide range of price variations. This study aims to compare the performance of three regression algorithms such as Random Forest, Gradient Boosting, and XGBoost, in building predictive models based on features such as land area, building area, number of bedrooms, bathrooms, and garage availability. The dataset analyzed consists of 1,642 entries, with house prices ranging from IDR 7 million to IDR 4.37 billion, an average price of IDR 1.14 billion, and a mode of IDR 775 million. Model evaluation was conducted using Mean Squared Error (MSE) and the coefficient of determination (R²), where XGBoost achieved the best performance with an MSE of 1.56 × 10¹⁴ IDR², an R² of 0.7746, and a Root Mean Squared Error (RMSE) of approximately IDR 12.5 million. These results indicate that XGBoost outperforms the other two models in handling complex tabular data and provides more accurate predictions. The predictive model has practical potential to be utilized by property developers, real estate agents, and local governments as a decision-support tool for price estimation, market evaluation, and data-driven urban planning. These findings highlight that selecting the appropriate algorithm can significantly enhance the quality of house price prediction. Abstrak Prediksi harga rumah menjadi tantangan penting dalam bidang properti, khususnya di wilayah Yogyakarta yang memiliki variasi harga cukup ekstrem. Penelitian ini bertujuan untuk membandingkan performa tiga algoritma regresi yaitu Random Forest, Gradient Boosting, dan XGBoost digunakan untuk membangun model prediksi harga rumah berdasarkan fitur seperti luas tanah, luas bangunan, jumlah kamar tidur, kamar mandi, dan garasi. Data yang dianalisis mencakup 1.642 entri dengan harga rumah berkisar antara Rp 7 juta hingga Rp 4,37 miliar, harga rata-rata sebesar Rp 1,14 miliar, dan modus Rp 775 juta. Evaluasi model dilakukan menggunakan metrik Mean Squared Error (MSE) dan koefisien determinasi (R²), di mana XGBoost menghasilkan performa terbaik dengan MSE sebesar 1,56 × 10¹⁴ rupiah², R² sebesar 0,7746, dan Root Mean Squared Error (RMSE) sekitar 12,5 juta rupiah. Hasil ini menunjukkan bahwa XGBoost lebih unggul dalam menangani data tabular kompleks dan memiliki akurasi prediksi yang lebih baik dibanding dua model lainnya. Model prediktif ini berpotensi digunakan oleh pengembang properti, agen real estate, maupun pemerintah daerah sebagai alat bantu dalam penetapan harga, evaluasi pasar, dan perencanaan tata ruang yang berbasis data. Temuan ini memberikan gambaran bahwa pemilihan algoritma yang tepat dapat meningkatkan kualitas prediksi harga properti.
Cross-Dataset Evaluation of Boosting Models for Hypertension Prediction Bety Wulan Sari; Dewi Ayu Murtiningsih; Donni Prabowo; Yoga Pristyanto; Ika Nur Fajri; Ike Verawati
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 4 (2026): August 2026 (in progress)
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i4.7620

Abstract

Hypertension remains a significant global risk factor for cardiovascular disease and related mortality, necessitating reliable early risk prediction models. Although boosting algorithms have demonstrated strong performance in structured medical data, limited studies have examined their consistency across heterogeneous datasets. This study aims to evaluate the cross-dataset performance and stability of three boosting models, such as XGBoost, LightGBM, and CatBoost, for hypertension prediction under multiple train–test split ratios. Two independent structured datasets were analyzed using 60:40, 70:30, 80:20, and 90:10 splits. To identify the optimal hyperparameters, grid search was performed using repeated stratified 5-fold cross-validation with three repetitions. Model effectiveness was measured using the evaluation metrics of accuracy, precision, recall, F1-score, and AUC. Results show that Dataset 1 gained consistently high predictive performance (accuracy > 0.98; AUC ≈ 1.00), indicating strong and well-separated predictive signals, whereas Dataset 2 demonstrated substantially lower discriminative ability (accuracy ≈ 0.71–0.72; AUC ≈ 0.50), suggesting limited predictive structure. Across both datasets, CatBoost consistently obtained the highest accuracy, particularly at the 90:10 split ratio. These findings demonstrate that dataset characteristics critically determine model effectiveness and that among the evaluated boosting algorithms, CatBoost delivered the strongest overall predictive performance.
Co-Authors Aji Setiawan Alfian Ramadhan Anggit Dwi Hartanto Anggit Dwi Hartanto Bety Wulan Sari Dewa Qintoro Dewi Ayu Murtiningsih Ema Utami Fajri, Ika Nur Firman Hidayat Fitri Ramdani Gagah Gumelar Hanif Al Fatta Ike Verawati Mu’alif Lihawa Norhikmah Norhikmah Nurul Ajeng Wiwi Widayani Yoga Pristyanto