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All Journal Jurnal Inti Talafa : Jurnal Teknik Informatika Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika JIKA (Jurnal Informatika)
Fahrizal Maulana
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Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Library & Information Science

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PRESENSI SISWA MENGGUNAKAN QR CODE DAN SMS BROADCAST BERBASIS WEB Sherly Christina; Agus Sehatman Saragih; Fahrizal Maulana
JURNAL TEKNOLOGI INFORMASI Vol 13 No 1 (2019): Jurnal Teknologi Informasi (JTI)
Publisher : Universitas Palangka Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (456.37 KB)

Abstract

Student attendance is one of the factors that need to be controlled by the School to maintain Indonesian resources. Therefore a school needs a system that can be used, for handles the students attendance data and distributes it to the parents. This necessity can be facilitated by building an online attendance system for the students. The online attendance system uses QR Code and Broadcast Short Message Service (SMS). The online attendance system used Waterfall methodology phases  as the system development  phases. Afterward  the system performance is evaluated using the black-box testing. The results test using black-box testing show that the system can accomplish the purpose of this research to meet the need of the School in collecting the student attendance data and distributing the information of the students attendance data to the parents. This system uses QR Code and the results of collecting the student attendance data are sent by the system through Broadcast SMS to parents.
PRESENSI SISWA MENGGUNAKAN QR CODE DAN SMS BROADCAST BERBASIS WEB Sherly Christina; Agus Sehatman Saragih; Fahrizal Maulana
Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Vol. 13 No. 1 (2019): Jurnal Teknologi Informasi Jurnal Keilmuan dan Aplikasi Bidang Teknik Informat
Publisher : Universitas Palangka Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47111/jti.v13i1.288

Abstract

Student attendance is one of the factors that need to be controlled by the School to maintain Indonesian resources. Therefore a school needs a system that can be used, for handles the students attendance data and distributes it to the parents. This necessity can be facilitated by building an online attendance system for the students. The online attendance system uses QR Code and Broadcast Short Message Service (SMS). The online attendance system used Waterfall methodology phases as the system development phases. Afterward the system performance is evaluated using the black-box testing. The results test using black-box testing show that the system can accomplish the purpose of this research to meet the need of the School in collecting the student attendance data and distributing the information of the students attendance data to the parents. This system uses QR Code and the results of collecting the student attendance data are sent by the system through Broadcast SMS to parents.
PRESENSI SISWA MENGGUNAKAN QR CODE DAN SMS BROADCAST BERBASIS WEB Sherly Christina; Agus Sehatman Saragih; Fahrizal Maulana
Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Vol. 13 No. 1 (2019): Jurnal Teknologi Informasi Jurnal Keilmuan dan Aplikasi Bidang Teknik Informat
Publisher : Universitas Palangka Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47111/jti.v13i1.288

Abstract

Student attendance is one of the factors that need to be controlled by the School to maintain Indonesian resources. Therefore a school needs a system that can be used, for handles the students attendance data and distributes it to the parents. This necessity can be facilitated by building an online attendance system for the students. The online attendance system uses QR Code and Broadcast Short Message Service (SMS). The online attendance system used Waterfall methodology phases as the system development phases. Afterward the system performance is evaluated using the black-box testing. The results test using black-box testing show that the system can accomplish the purpose of this research to meet the need of the School in collecting the student attendance data and distributing the information of the students attendance data to the parents. This system uses QR Code and the results of collecting the student attendance data are sent by the system through Broadcast SMS to parents.
HYPERPARAMETER MODEL LSTM-GRU UNTUK PREDIKSI PEMETAAN TINGKAT KEBAKARAN HUTAN maulana, fahrizal; kusrini, kusrini
Jurnal Informatika Vol 9, No 1 (2025): JIKA (Jurnal Informatika)
Publisher : University of Muhammadiyah Tangerang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31000/jika.v9i1.12882

Abstract

Bencana kebakaran hutan merupakan permasalahan besar bagi pemerintah provinsi Kalimantan Tengah. Langkah eksternal maupun internal telah dilakukan melalui kebijakan publik yang dibuat berupa hasil prediksi atau pemetaan kebakaran hutan dimasa akan datang. Dalam penelitian ini dilakukan pengembangan model untuk prediksi tren dan pemetaan tingkat kebakaran hutan dengan fokus penerapan hyperparameter terhadap kombinasi RNN di dua perangkat dan pengaturan rasio dataset berbeda. Dataset yang digunakan merupakan penggabungan dataset MODIS dan Merra2 sebagai end-to-end multivariate fitur dan target. Penggabungan dataset menggunakan asas interpolasi untuk mendukung kontinuitas kekosongan data. Untuk mencapai tujuan penelitian dilakukan eksperimental sebanyak 12 skenario terhadap 6 set pengaturan hyperparameter dengan evaluasi menggunakan performansi regresi MAE dan RMSE. Temuan penelitian menunjukan model kombinasi LSTM-GRU konsisten memperoleh rata-rata error MAE 2% dan RMSE 6% pada P1 dan P2 dengan nilai performa loss pembelajaran terbaiknya berada pada skenario 7, 10, 11 untuk  pembagian kedua dataset dan skenario 8 di rasio dataset 70:30. Pengujian di perangkat berbeda juga tidak mempengaruhi penurunan error pada model terhadap penerapan hyperparameter kecuali lama runtime pembelajaran model. Hasil penelitian ini memberikan gambaran yang komprehensif terhadap pemilihan parameter terhadap kombinasi model RNN yang ideal berdasarkan pembagian rasio dataset serta memberikan pemahaman tentang penerapan hyperparameter pada perangkat berbeda.
ANALISIS POLA PENYEBAB KARHUTLA KALIMANTAN TENGAH MENGGUNAKAN INDOBERT DAN CAUSAL PATTERN MINING Fahrizal Maulana; Kusrini; Ika Safitri Windiarti; Sutami
INTI TALAFA Vol. 18 No. 2 (2026): Vol 18 No 2 Tahun 2026
Publisher : Program Studi Teknik Informatika Universitas Muhammadiyah Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32534/int.v18i2.8638

Abstract

Forest and land fires in Central Kalimantan are environmental problems influenced by natural factors and human activities, resulting in complex causal relationship patterns. This study aims to analyze wildfire cause patterns in Central Kalimantan using an IndoBERT-based NLP and AI approach integrated with causal pattern mining on online news articles. Data were collected through web scraping from six local and national news portals using nine Google search queries related to wildfires, resulting in 436 relevant articles as the main corpus. The methodological stages included text preprocessing, semantic representation using a 768-dimensional IndoBERT transformer model, topic discovery using BERTopic, and causal pattern analysis through co-occurrence analysis and contextual relation mining. The topic discovery results identified 10 main topics, with the topic “extreme dry season” dominating with 104 data points, followed by “hot weather and drought” (54 data points). Anthropogenic factors such as intentional land burning, land clearing activities, and human negligence were also identified as significant causes. Contextual relation mining results showed that the words “land” (1,473 occurrences), “smoke” (938), “dry season” (334), and “peatland” (288) were the most dominant causal contexts, while co-occurrence analysis generated 37,581 word pairs forming a causal network. Future studies are recommended to integrate spatial data, expand the dataset, and implement knowledge graphs to support real-time wildfire disaster intelligence systems.
ANALISIS POLA PENYEBAB KARHUTLA KALIMANTAN TENGAH MENGGUNAKAN INDOBERT DAN CAUSAL PATTERN MINING Fahrizal Maulana; Kusrini; Ika Safitri Windiarti; Sutami
INTI TALAFA Vol. 111 No. 111 (2026)
Publisher : Program Studi Teknik Informatika Universitas Muhammadiyah Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32534/int.v18i2.8638

Abstract

Forest and land fires in Central Kalimantan are environmental problems influenced by natural factors and human activities, resulting in complex causal relationship patterns. This study aims to analyze wildfire cause patterns in Central Kalimantan using an IndoBERT-based NLP and AI approach integrated with causal pattern mining on online news articles. Data were collected through web scraping from six local and national news portals using nine Google search queries related to wildfires, resulting in 436 relevant articles as the main corpus. The methodological stages included text preprocessing, semantic representation using a 768-dimensional IndoBERT transformer model, topic discovery using BERTopic, and causal pattern analysis through co-occurrence analysis and contextual relation mining. The topic discovery results identified 10 main topics, with the topic “extreme dry season” dominating with 104 data points, followed by “hot weather and drought” (54 data points). Anthropogenic factors such as intentional land burning, land clearing activities, and human negligence were also identified as significant causes. Contextual relation mining results showed that the words “land” (1,473 occurrences), “smoke” (938), “dry season” (334), and “peatland” (288) were the most dominant causal contexts, while co-occurrence analysis generated 37,581 word pairs forming a causal network. Future studies are recommended to integrate spatial data, expand the dataset, and implement knowledge graphs to support real-time wildfire disaster intelligence systems.
ANALISIS POLA PENYEBAB KARHUTLA KALIMANTAN TENGAH MENGGUNAKAN INDOBERT DAN CAUSAL PATTERN MINING Fahrizal Maulana; Kusrini; Ika Safitri Windiarti; Sutami
INTI TALAFA Vol. 333 No. 333 (2026)
Publisher : Program Studi Teknik Informatika Universitas Muhammadiyah Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32534/int.v18i2.8638

Abstract

Forest and land fires in Central Kalimantan are environmental problems influenced by natural factors and human activities, resulting in complex causal relationship patterns. This study aims to analyze wildfire cause patterns in Central Kalimantan using an IndoBERT-based NLP and AI approach integrated with causal pattern mining on online news articles. Data were collected through web scraping from six local and national news portals using nine Google search queries related to wildfires, resulting in 436 relevant articles as the main corpus. The methodological stages included text preprocessing, semantic representation using a 768-dimensional IndoBERT transformer model, topic discovery using BERTopic, and causal pattern analysis through co-occurrence analysis and contextual relation mining. The topic discovery results identified 10 main topics, with the topic “extreme dry season” dominating with 104 data points, followed by “hot weather and drought” (54 data points). Anthropogenic factors such as intentional land burning, land clearing activities, and human negligence were also identified as significant causes. Contextual relation mining results showed that the words “land” (1,473 occurrences), “smoke” (938), “dry season” (334), and “peatland” (288) were the most dominant causal contexts, while co-occurrence analysis generated 37,581 word pairs forming a causal network. Future studies are recommended to integrate spatial data, expand the dataset, and implement knowledge graphs to support real-time wildfire disaster intelligence systems.
Co-Authors Agus Sehatman Saragih Amega Yasutra Anak Agung Istri Sri Wiadnyani Ika Safitri Windiarti Jon Felix Turnip Kusrini Kusrini Sherly Christina Sherly Christina Sri Dwi Mustaqim Sutami