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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Proceeding International Conference on Information Technology and Business International conference on Information Technology and Business (ICITB) MUDRA Jurnal Seni Budaya Matrix : Jurnal Manajemen Teknologi dan Informatika METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Logic : Jurnal Rancang Bangun dan Teknologi EDUMATIC: Jurnal Pendidikan Informatika International Journal of Engineering and Emerging Technology International Journal of Applied Sciences and Smart Technologies Indonesian Journal of Electrical Engineering and Computer Science MATRIX : JURNAL MANAJEMEN TEKNOLOGI DAN INFORMATIKA Jurnal Informatika Polinema (JIP)
Putu Manik Prihatini
Politeknik Negeri Bali
Religion Arts Humanities 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 Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mechanical Engineering Social Sciences

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Dashboard Analisis Sentimen Ulasan Google Maps Perguruan Tinggi di Bali Berbasis IndoBERT I Made Pradnyananda Kevin Arnawa; Putu Manik Prihatini; Yessi Aprilia Waluyo
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 10 No. 2 (2026): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol10No2.pp535-542

Abstract

Public opinions shared on the Google Maps platform factually reflect the reputation and service quality of higher education institutions in Bali Province. However, manually analyzing thousands of reviews is time-consuming and potentially subjective. Therefore, this research focuses on developing a web-based system equipped with an interactive visual dashboard to computerize the categorization and comparison of review sentiments. The text classification process is built using the IndoBERT (Indonesian Bidirectional Encoder Representations from Transformers) deep learning architecture, which sorts opinions into positive or negative sentiments before presenting them on the web interface. Algorithm performance evaluation shows that IndoBERT is reliable, achieving an accuracy rate of 82.18% and a sensitivity (recall) of 91.90%. The modeling output is visualized dynamically through a dashboard, enabling institutions and prospective students to monitor campus sentiments more efficiently, objectively, and precisely.
Pengembangan Model Prediksi Harga Kos di Wilayah Jimbaran Menggunakan Metode Decision Tree Regression I Putu Krishna Paradipta; Putu Manik Prihatini; Ni Nyoman Harini Puspita
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 10 No. 2 (2026): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol10No2.pp597-606

Abstract

The demand for temporary housing in Jimbaran continues to increase, making it more complex to determine boarding house prices due to various physical characteristics, location, and facilities. Previous studies have limitations in modeling nonlinear relationships among variables and have not integrated feature engineering and hyperparameter tuning to improve prediction performance. This study aims to develop a model for predicting boarding house prices using the decision tree regression algorithm. The model was developed through data preprocessing, feature engineering, and hyperparameter tuning using GridSearchCV with stratified K-fold cross-validation. The optimal hyperparameter combination was max_depth = 8, min_samples_split = 5, min_samples_leaf = 2, and max_leaf_nodes = 80. The model achieved R² values of 0.8871 for the training data and 0.8326 for the testing data, with an MAE of IDR 225,438 and an RMSE of IDR 303,080. The R² difference of 0.0546 indicates satisfactory generalization without significant overfitting. In addition to producing an accurate prediction model, this study contributes through the implementation of data preprocessing, feature engineering, hyperparameter tuning, and feature importance analysis to identify the factors that most significantly affect boarding house prices.
Model Prediksi Risiko Kredit Macet Menggunakan XGBoost Dengan AI Explainer Berbasis SHAP Alvian Maulana Mansyur; Putu Manik Prihatini; Gde Brahupadhya Subiksa
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 10 No. 2 (2026): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol10No2.pp635-644

Abstract

Credit is a financial instrument that carries the risk of default when borrowers are unable to fulfill their repayment obligations. This risk may lead to financial losses for lending institutions, making accurate credit risk prediction essential. This study aims to develop a credit risk prediction model using Extreme Gradient Boosting (XGBoost) and point out its predictions using an AI explainer based on SHapley Additive exPlanations (SHAP). The dataset was obtained from the internal database of a fintech company in Indonesia and consisted of 116,250 records with 19 attributes. The research stages included Exploratory Data Analysis (EDA), missing value and outlier handling, data splitting, model training, evaluation, and result interpretation. The XGBoost model achieved an accuracy of 92%. For the Less Current class, the model obtained a precision of 0.91, a recall of 0.97, and an F1-score of 0.94. For the current class, the model achieved a precision of 0.86, a recall of 0.62, and an F1-score of 0.72. For the Default class, the model obtained a precision of 0.97, a recall of 0.91, and an F1-score of 0.94. SHAP analysis as an AI explainer revealed that the income and side_income features were the most influential factors in determining credit risk.
Co-Authors Alvian Maulana Mansyur Gde Brahupadhya Subiksa Harry Saptarini, Ni Gusti Ayu Putu I ketut Gede Darma Putra I Ketut Gede Sudiartha I Ketut Gede Sudiartha I Made Agus Budiarta I Made Gede Weda Pradnyana I Made Pradnyananda Kevin Arnawa I Made Purbhawa I Nyoman Mandia I Putu Krishna Paradipta I Putu Oka Wisnawa Ida Ayu Dwi Giriantari Ida Bagus Adisimakrisna Peling M Sudarma Ni Gusti Ayu Putu Harry Saptarini Ni Luh Putu Listya Dewi Ni Nyoman Harini Puspita Ni Wayan Widyasari Damayanti Nyoman Dewi Pebryani Ratna Cora Sudharsana, Tjok Istri Suryawan, I Ketut Yessi Aprilia Waluyo