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All Journal Statistika Jurnal Gaussian (JRAMB) Jurnal Riset Akuntansi Mercu Buana INFORMATIKA Jurnal Riset Sistem Informasi
Trimono Trimono
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Other

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Journal : INFORMATIKA

 1 
SEGMENTASI WILAYAH INDONESIA BERDASARKAN IHK MENGGUNAKAN AHC DAN SPECTRAL CLUSTERING Shafira Amanda Putri; Tsabita Rosyidah Putri; Trimono Trimono; Muhammad Idhom
Informatika: Jurnal Teknik Informatika dan Multimedia Vol. 5 No. 1 (2025): MEI : JURNAL INFORMATIKA DAN MULTIMEDIA
Publisher : LPPM Politeknik Pratama Kendal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/informatika.v5i1.1010

Abstract

Social and economic disparities between regions in Indonesia are still a serious problem, reflected in the high Gini Ratio and disparities in purchasing power. The Consumer Price Index (CPI) is an important indicator in measuring consumption patterns and price pressures between regions. This study aims to cluster 150 districts/cities based on consumption patterns through the CPI using the Agglomerative Hierarchical Clustering (AHC) and Spectral Clustering methods combined with Principal Component Analysis (PCA). The innovation lies in the comparison of the two clustering methods as well as the application of PCA to clarify the data structure. Evaluation with Silhouette Score and Davies-Bouldin Index showed that AHC gave the best results with four representative clusters (Silhouette: 0.76; DBI: 0.32), compared to Spectral Clustering with three clusters (Silhouette: 0.75; DBI: 0.54). Each cluster has different expenditure characteristics, useful for data-driven policy making. These results show that AHC is more effective in capturing interregional variations in consumption.
PREDIKSI HARGA PENUTUPAN SAHAM BBRI DENGAN MODEL HYBRID LSTM-XGBOOST Nabilah Selayanti; Dwi Amalia Putri; Trimono Trimono; Mohammad Idhom
Informatika: Jurnal Teknik Informatika dan Multimedia Vol. 5 No. 1 (2025): MEI : JURNAL INFORMATIKA DAN MULTIMEDIA
Publisher : LPPM Politeknik Pratama Kendal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/informatika.v5i1.1011

Abstract

The ease of investing in the digital era has driven Generation Z to dominate stock market participation, particularly in blue-chip stocks such as PT Bank Rakyat Indonesia Tbk (BBRI). However, stock price fluctuations influenced by macroeconomic factors, regulations, and global market sentiment make it difficult for investors to make accurate decisions. Decisions based on insufficient information pose a significant risk of loss, especially for novice investors. This study proposes a hybrid LSTM-XGBoost approach for predicting BBRI stock prices, combining the strengths of LSTM in capturing nonlinear time series patterns and XGBoost's effectiveness in reducing prediction errors. The model leverages both historical data and feature extraction outputs from the LSTM model. Future stock price values are then predicted by XGBoost using this combined dataset. The Hybrid LSTM XGBoost model outperforms the individual base models in terms of prediction accuracy, achieving an RMSE of 117.89, MAE of 92.45, and MAPE of 2.21%.
PERBANDINGAN KINERJA GRU DAN SVR UNTUK PREDIKSI EMAS DI INDONESIA Mohammad Sufa Ammar Habibi; Arindra Harris Abdillah; Mohammad Idhom; Trimono Trimono
Informatika: Jurnal Teknik Informatika dan Multimedia Vol. 5 No. 1 (2025): MEI : JURNAL INFORMATIKA DAN MULTIMEDIA
Publisher : LPPM Politeknik Pratama Kendal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/informatika.v5i1.1105

Abstract

Emas merupakan instrumen investasi yang banyak diminati di Indonesia, terutama saat terjadi ketidakstabilan ekonomi. Namun, volatilitas harga emas yang dipengaruhi oleh faktor makroekonomi domestik dan global membuat prediksinya menjadi tantangan tersendiri. Penelitian ini membandingkan kinerja dua model prediksi, yaitu Gated Recurrent Unit (GRU) dan Support Vector Regression (SVR), dalam meramalkan harga emas jangka pendek berdasarkan data historis harian periode 2020–2025 sebanyak 1.345 data. Data diolah melalui proses normalisasi dan pembentukan data sekuensial dengan jendela waktu 60 hari. Kedua model dievaluasi menggunakan metrik regresi seperti RMSE, MAE, MSE, dan R². Hasil menunjukkan bahwa model GRU lebih unggul dibandingkan SVR dalam menangkap pola non-linear dan temporal pada data deret waktu, serta menghasilkan prediksi yang lebih akurat. Harga emas pada 7 Mei 2025 diperkirakan sebesar Rp1.736.978. Temuan ini menunjukkan bahwa model deep learning seperti GRU memiliki potensi besar dalam analisis data keuangan dan dapat memberikan kontribusi praktis bagi investor, peneliti, dan pembuat kebijakan. Penelitian selanjutnya disarankan untuk mengintegrasikan variabel makroekonomi dan pendekatan hybrid guna meningkatkan akurasi prediksi.
PERBANDINGAN ALGORITMA K-PROTOTYPES DENGAN AGGLOMERATIVE CLUSTERING DALAM SEGMENTASI SISWA BERDASARKAN FAKTOR AKADEMIK DAN SOSIAL Muchamad Risqi; , Muhammad Nashif Farid; , Mohammad Idhom; Trimono Trimono
Informatika: Jurnal Teknik Informatika dan Multimedia Vol. 5 No. 1 (2025): MEI : JURNAL INFORMATIKA DAN MULTIMEDIA
Publisher : LPPM Politeknik Pratama Kendal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/spkmfd39

Abstract

Student performance is affected by internal and external factors such as study time, absenteeism, tutoring, and parental support—factors often overlooked by traditional education methods. This study applies K-Prototypes and Agglomerative Clustering with Gower Distance to segment students using mixed-type data. Five key variables were analyzed: study time, absences, GPA, tutoring, and parental support. The Elbow Method was used to identify the optimal number of clusters, and Silhouette Score to evaluate performance. Results show K-Prototypes outperformed Agglomerative Clustering (0.332 vs 0.186). Three student segments emerged: active students with average GPA, low-risk learners, and high-achievers with minimal external support. These findings can inform more adaptive and data-driven academic interventions for education stakeholders.
Co-Authors , Mohammad Idhom , Muhammad Nashif Farid Arindra Harris Abdillah Cenditya Ayu Aurelia Di Asih I Maruddani Di Asih I Maruddani Dwi Amalia Putri Dwi Ispriyanti Gunawan, Jovanka Vania I Gede Susrama Susrama Mas Diyasa Khufi, Rijal Apuila Mohammad Idhom Mohammad Sufa Ammar Habibi Muchamad Risqi Muhammad Idhom Muhammad Nasrudin Nabilah Selayanti Nainggolan, Ester Yunita Shafira Amanda Putri Tsabita Rosyidah Putri