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All Journal Jurnal Ilmu Komputer dan Informasi JIKSI (Jurnal Ilmu Komputer dan Sistem Informasi) Computatio : Journal of Computer Science and Information Systems ComTech: Computer, Mathematics and Engineering Applications Jurnal Teknik Informatika (JUTIF) TIERS Information Technology Journal
Lely Hiryanto
Fakultas Teknologi Informasi Universitas Tarumanagara Jakarta - Indonesia
Computer Science & IT Engineering Mathematics Other

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PREDIKSI CURAH HUJAN DI KABUPATEN BADUNG, BALI MENGGUNAKAN METODE LONG SHORT-TERM MEMORY Brando Dharma Saputra; Lely Hiryanto; Teny Handhayani
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 11 No. 2 (2023): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v11i2.26002

Abstract

Rainfall is the height of rainwater that falls on a flat area, assuming it doesn't evaporate, doesn't seep, and doesn't flow. Rain levels are measured in mm (millimeters). The target of the research being conducted is in Badung Regency, Bali because Bali is a tourist area that is often visited by tourists and from Indonesian itself, so predictions of meteorology, such as rainfall will greatly impact tourism. In this test, predictions use the Long Short Term Memory (LSTM) method, using daily weather data from the BMKG from 2010 to 2020 as training data and daily weather data for 2021 as prediction data. Based on the test results above, the results show that the two LSTM tests with LSTM Model 128.64 and LSTM Model 64.32 have low MAE and MAPE error values. From First Scenario, the Mean Absolute Error (MAE) value is 8.97246598930908 and Mean Absolute Percentage Error (MAPE) value is 1.7657206683278308%. From Second Scenario, the Mean Absolute Error is 9.706669940783014 and Mean Absolute Percentage Error is 1.9028466692362323%. From the MAE and MAPE values obtained in these two scenarios, it can be proven that from the evaluation results of Rainfall predictions in Badung Regency, Bali, the predictions can be said to be very accurate because they have an error value of less than 10.
Aplikasi Monitoring Tunggakan Uang Kuliah Mahasiswa Non Aktif Di Universitas Tarumanagara Menggunakan Metode Naive Bayes Timothy Reynaldi; Lely Hiryanto; Darius Andana Haris
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 11 No. 2 (2023): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v11i2.26005

Abstract

Universitas Tarumanagara memiliki dua status mahsiswa, yaitu mahasiswa aktif dan mahasiswa non aktif. Saat ini, bidang administrasi di Universitas Tarumanagara belum memiliki sistem yang baik untuk menangani tunggakan uang kuliah dari mahasiswa non aktif. Tujuan dari perancangan Aplikasi Monitoring Tunggakan Uang Kuliah Mahasiswa Non Aktif ini adalah untuk memperbaiki dan memudahkan user untuk memonitoring tunggakan uang kuliah dari mahasiswa non aktif di Universitas Tarumanagara. Aplikasi ini menggunakan metode Naive Bayes. Penerapan dari metode Naive Bayes ini berfungsi untuk menghitung probabilitas kemungkinan mahasiswa Universitas Tarumanaga yang non aktif selama tiga semester berturut-turut harus di keluarkan atau tidak. Hasil dari penerapan metode Naive Bayes ini berhasil untuk menampilkan output prediksi untuk dikeluarkan atau dilanjutkannya mahasiswa yang sudah non aktif selama tiga semester berturut-turut. Hasil dari pengujian fungsional aplikasi menggunakan mendapatkan output sukses untuk pengetesan pada semua halaman yang di uji dan metode pengambilan keputusan dari aplikasi ini memiliki akurasi untuk prediksi tindakan pengambilan keputusan sebesar 91%.
IMPLEMENTASI AES UNTUK KEAMANAN APLIKASI FORMULIR ONLINE Andri Firnandius; Lely Hiryanto
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 11 No. 2 (2023): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v11i2.26011

Abstract

Google Forms software is an online application which users can create form for various purpose. The application can store information or data that has been provided by the form fillers. The form fillers are merely identified by their institutional email's domain or those with the access link to the make response for each question provided in an online form. The use of third-party applications certainly reduces the sense of trust in the security of the data provided. Therefore, a digital form application design was created with the Advanced Encryption Standard (AES). The aim is to maintain the security of the data provided by the form filler and ensure that the fillers are those with the authority.
Solving an Optimization Problem of Image View Layout with Priority using Heuristic Approach Hiryanto, Lely; Wirawan, Andhika Putra; Lee, Viciano
TIERS Information Technology Journal Vol. 6 No. 1 (2025)
Publisher : Universitas Pendidikan Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38043/tiers.v6i1.6519

Abstract

The image view layout with priority (IVLP) problem focuses on efficiently arranging picture cards of uniform height but varying widths into the minimum number of 2D frames or display sets and prioritizing images with higher priority to be placed at the earlier displays. We mathematically modeled IVLP using integer linear programming. To approximate IVLP solutions, we introduce a greedy-based heuristic, Best-Fit-IVLP (BFI), and a swarm optimization algorithm, Ant Colony Optimization (ACO). BFI allocates picture cards in descending order of priority and width for each display line, seeking another card that can optimally fill the remaining space on each line. In contrast, ACO randomly arranges cards from high to low priority within every line. Experimental results using different numbers of SVG images indicate that BFI and ACO generate solutions close to optimal. BFI demonstrates superior practicality, executing significantly faster than ACO; for 160 images, BFI runs in 0.00044 seconds compared to ACO's 117.93 seconds. Both BFI and ACO achieve space utility rates ranging from 0.578 to 0.8. While BFI consistently produces the same card arrangement, ACO offers diverse arrangements for identical optimal display set counts and space utilization.
Forecasting Indonesian Banking Stock Prices Using Prophet, XGBoost, and Ridge Regression: A Comparative Analysis Tony, Tony; Ratchagit, Manlika; Hiryanto, Lely
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.4973

Abstract

This study investigates the efficacy of Prophet, XGBoost, and Ridge Regression in forecasting stock prices of four major Indonesian banks—Bank Central Asia (BBCA.JK), Bank Negara Indonesia (BBNI.JK), Bank Rakyat Indonesia (BBRI.JK), and Bank Mandiri (BMRI.JK)—using daily historical data from January 2020 to March 2025, sourced from Yahoo Finance. The banking sector's volatility, evidenced by year-to-date declines ranging from 7.59% (BBCA) to 22.69% (BMRI) as of May 1, 2025, underscores the need for accurate predictive models. Performance was evaluated using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), revealing Ridge Regression as the superior method, consistently achieving the lowest errors (i.e., MAE of 23.81 for BBNI.JK and RMSE of 55.75 for BBCA.JK). Prophet exhibited the highest errors, suggesting its seasonal focus is less suited to stock price unpredictability, while XGBoost performed moderately better but lagged behind Ridge Regression. The results highlight Ridge Regression’s effectiveness in handling multicollinearity and noise in financial data. Our discussions emphasize the importance of model selection based on data characteristics, with implications for investment decision-making in the Indonesian market. This research contributes to the field of computational finance by providing a comparative analysis that not only identifies Ridge Regression as a superior method for forecasting stock prices but also illuminates the limitations of popular models like Prophet and XGBoost in handling financial data's unique characteristics, guiding future model selection and development. Future research could explore hybrid models to enhance accuracy across varied market conditions, addressing the study’s 60-day forecasting horizon limitation.
Sistem Rekomendasi Deposito Berjangka Menggunakan Metode Random Forest: Studi Kasus Pada Bank Marketing UCI Aurelia; Hiryanto, Lely
Computatio : Journal of Computer Science and Information Systems Vol. 9 No. 1 (2025): Computatio: Journal of Computer Science and Information Systems
Publisher : Faculty of Information Technology, Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/computatio.v9i1.32667

Abstract

Perkembangan teknologi dan perubahan perilaku konsumen di era digital telah mendorong industri perbankan untuk mengadopsi pendekatan yang lebih inovatif dalam menawarkan produk dan layanan mereka, terutama deposito berjangka. Penelitian ini bertujuan untuk mengembangkan sistem rekomendasi deposito berjangka menggunakan metode Random Forest, dengan studi kasus pada dataset Bank Marketing UCI. Sistem ini dirancang untuk memberikan rekomendasi yang akurat dan personal berdasarkan data historis, preferensi, dan karakteristik individu nasabah. Metode Random Forest digunakan karena kemampuannya dalam menangani data besar dan kompleks, serta mengurangi risiko pemodelan berlebihan. Dataset yang digunakan mencakup 45.211 instances dengan 17 atribut, termasuk informasi demografis nasabah dan riwayat interaksi dengan bank. Hasil penelitian menunjukkan bahwa model Random Forest yang dikembangkan mencapai akurasi 91.56%, dengan presisi 65.47%, recall 52.12%, dan F1-score 58.04%. Analisis variable importance measure mengidentifikasi 'duration', 'age', dan 'balance' sebagai faktor paling berpengaruh dalam keputusan nasabah untuk berlangganan deposito berjangka. Implementasi sistem ini diharapkan dapat meningkatkan efektivitas pemasaran produk deposito berjangka dan meningkatkan kepuasan nasabah.
Co-Authors Alfine Candra Cuaca Anak Agung Gede Sugianthara Andre Widjaya Andri Firnandius Andri Muliawan Ardhytia Satria Nugraha Arnold Pramudita Tjiawi Aurelia Bagus Mulyawan bagus Mulyawan Bobby Tumbelaka Bobby Tumbelaka Brando Dharma Saputra Chairisni Lubis Chandra Wijaya Chandra Wijaya Chandra Wijaya Chintia Yusnita Violetta Darius Andana Haris Dedi Trisnawarman Desi Arisandi Dian Anggraini Cahyaningtyas Dya Erny Herwindiati Dyah Erny Herwindiati Dyah Erny Herwindiati Elizabeth Erlsha Elizabeth Erlsha, Elizabeth Ericko Satyagraha Ericko Satyagraha Farenco Farenco Farenco Farenco Ferryanto Ferryanto Ferryanto Ferryanto Fika Alfiani Frankie Frankie Frankie Frankie, Frankie Fransisca Regina Fransisca Regina, Fransisca Gabriel Fransisco Gabriel Fransisco, Gabriel Grimaldi Suryadi Grimaldi Suryadi Gunadi Gan Gunadi Gan Harprori Patti Irawati Djajadi Irawati Djajadi Isa Iskandar Jacklin Sinthia Thio James Ariel Gunawan James Ariel Gunawan, James Ariel Janson Hendryli Jason Djatmiko Josselyn Sinthia Thio Karendef Karendef Kristianto, Hans Kurniawan Sulianto Lee, Viciano Lina Lina Listovie Cavito Mariana - Mariana Mariana Martono Darsono Martono Darsono Mishelle Tirtajaya Winartha Nadia Yanitra Nadya Yanitra, Nadya Pharadya Ajeng Swari Sukmawati Ratchagit, Manlika Renaldo Ali Renaldo Ali, Renaldo Riki Yohanes Hendriyanto Rionaldy Trisaputra Rosalinda . Rosalinda Rosalinda Satrya N. Ardhytia Stephanie Budianto Stephen Yan Putra Halim, Stephen Yan Putra Stevy Lie Stevy Lie, Stevy Sufisan Sufisan TATI NURHAYATI Teny Handhayani Timothy Reynaldi Tony Tony Tony Tony TRI SUTRISNO Vina Tandean Viny Christanti M Wirawan, Andhika Putra Yunita Yunita Yunita Yunita