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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Ilmu dan Teknologi Kelautan Tropis IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Informatika Jurnal Simetris Elkom: Jurnal Elektronika dan Komputer Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) JTSL (Jurnal Tanah dan Sumberdaya Lahan) Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Transformatika Jurnal Edukasi dan Penelitian Informatika (JEPIN) Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Sinkron : Jurnal dan Penelitian Teknik Informatika INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi JURNAL MEDIA INFORMATIKA BUDIDARMA Faktor Exacta Jurnal Ilmiah Matrik JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Indonesian Journal of Computing and Modeling J-SAKTI (Jurnal Sains Komputer dan Informatika) JURIKOM (Jurnal Riset Komputer) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Building of Informatics, Technology and Science Journal Sensi: Strategic of Education in Information System JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) TIN: TERAPAN INFORMATIKA NUSANTARA Aiti: Jurnal Teknologi Informasi Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Teknik Informatika (JUTIF) Journal of Information Technology (JIfoTech) J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Info Sains : Informatika dan Sains Jurnal Nasional Teknik Elektro dan Teknologi Informasi IT-Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi Jurnal Informatika: Jurnal Pengembangan IT Jurnal Indonesia : Manajemen Informatika dan Komunikasi Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Lontar Komputer: Jurnal Ilmiah Teknologi Informasi
Sri Yulianto Joko Prasetyo
Universitas Kristen Satya Wacana
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Predicting the Number of Passengers in Public Transportation Areas Using the Deep Learning Model LSTM Joko Siswanto; Sri Yulianto Joko Prasetyo; Sutarto Wijono; Evi Maria; Untung Rahardja
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol. 15 No. 03 (2024): Vol. 15, No. 03 December (2024)
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2024.v15.i03.p03

Abstract

Accurate predictions of the number of public transport passengers on buses in each region are crucial for operations. They are required by the planning and management authority for bus public transport. A deep learning-based LSTM prediction model is proposed to predict the number of passengers in 4 bus public transportation areas (central, north, south, and west), evaluated by MSLE, MAPE, and SMAPE with dropout, neuron, and train-test variations. The CSV dataset obtained from Auckland Transport(AT) New Zealand metro patronage report on bus performance(1/01/2019-31/07/2023) is used for evaluation. The best prediction model was obtained from the lowest evaluation value and relatively fast time with a dropout of 0.2, 32 neurons, and train-test 80-20. The prediction model on training and testing data improves with the suitability of tuning for four predictions for the next 12 months with mutual fluctuations. The strong negative correlation is central-south, while the strong positive correlation is north-west. Predictions are less closely interconnected and dependent, namely central-south. With its potential to significantly impact policy-making, this prediction model can increase public transport mobility in each region, leading to a more efficient and accessible public transport system and ultimately enhancing the public's daily lives. This research has practical implications for public transport authorities, as it can guide them in making informed decisions about service planning and resource allocation.
Tsunami Vulnerability and Risk Assessment in Banyuwangi District using machine learning and Landsat 8 image data Gallen cakra adhi wibowo; Sri Yulianto Joko Prasetyo; Irwan Sembiring
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 22 No. 2 (2023)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i2.2677

Abstract

The tsunami is a disaster that often occurs in Indonesia, there are no valid indicators to assess and monitor coastal areas based on functional land use and based on land cover which refers to the biophysical characteristics of the earth's surface. One of the recommended methods is the vegetation index. Vegetation index is a method from LULC that can be used to provide information on how severe the impact of the tsunami was on the area.In this study, an increase in the vegetation index was carried out using machine learning. The purpose of this study was to develop a tsunami vulnerability assessment model using the Vegetation Index extracted from Landsat 8 satellite imagery optimized with KNN, Random Forest and SVM. The stages of study, are: 1)extraction Landsat 8 images using algorithms NDVI, NDBI, NDWI, MSAVI, and MNDWI; 2) prediction of vegetation indices using KNN, Random Forest, and SVM algorithms. 3) accuracy testing using the MSE, RMSE, and MAE,4) spatial prediction using the Kriging function and 5) tsunami modelling vulnerability indicators. The results of this study indicate that the NDVI interpolation value is 0 - 0.1 which is defined as vegetation density, biomass growth, and moderate to low vegetation health. the NDWI value is 0.02 - 0.08 and the MNDWI value is 0.02 - 0.09 which is interpreted as the presence of surface water along the coast. MSAVI is a value of 0.1 – 0 which is defined as the absence of vegetation. The NDBI interpolation value is -0.05 - (-0.08) which is interpreted as the existence of built-up land with social and economic activities. From the results of research on the 10 areas studied, there are 3 areas with conditions that have a high level of tsunami vulnerability. 2 areas with medium vulnerability and 5 areas with low vulnerability to tsunami.
Perbandingan Kinerja Algoritma Klasifikasi untuk Pemilihan Tempat Promosi upaya Meningkatkan Jumlah Mahasiswa Baru Fadilah, Nurul; Joko Prasetyo, Sri Yulianto; Kristianto, Budhi
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 13 No 1: Februari 2026
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2026131

Abstract

Efektivitas strategi promosi menjadi kunci keberhasilan institusi pendidikan tinggi dalam menarik minat calon mahasiswa baru. Namun, banyak institusi menghadapi masalah serius dalam pemilihan lokasi promosi yang tepat, yang mengakibatkan penurunan minat calon mahasiswa dan pemborosan sumber daya. Untuk mengatasi masalah ini, penelitian ini mengembangkan model klasifikasi berbasis data mining yang mampu mengidentifikasi lokasi promosi paling efektif. Tiga algoritma klasifikasi yang digunakan dalam penelitian ini adalah Logistic Regression, Support Vector Machine (SVM), dan Decision Tree (C4.5). Data pendaftaran mahasiswa dikumpulkan dan diproses melalui tahapan pra pemprosesan yang meliputi penggantian nilai hilang, normalisasi data, dan transformasi atribut nominal menjadi numerik. Data kemudian dibagi menjadi subset pelatihan dan pengujian menggunakan metode split data dengan rasio 70:30. Hasil evaluasi menunjukkan bahwa model Decision Tree (C4.5) memberikan performa terbaik dengan accuracy 93.75%, precision 97.37%, dan recall 90.24%. Logistic Regression juga menunjukkan hasil yang memuaskan dengan accuracy 90.00%, precision 92.31%, dan recall 87.80%. Sementara itu, SVM menunjukkan performa yang lebih rendah dengan accuracy 72.50%, precision 80.65%, dan recall 60.98%. Kesimpulannya, model Decision Tree (C4.5) dan Logistic Regression dapat diandalkan untuk mengoptimalkan strategi promosi institusi pendidikan tinggi, memastikan alokasi sumber daya yang lebih efisien dan efektif, serta meningkatkan jumlah pendaftar baru. Penelitian ini juga memberikan kontribusi signifikan dalam literatur terkait penggunaan data mining untuk strategi promosi di sektor pendidikan tinggi.   Abstract The effectiveness of promotional strategies is crucial for higher education institutions in attracting new student enrollments. However, many institutions face serious issues in selecting the appropriate promotional locations, leading to decreased student interest and resource wastage. To address this issue, this study develops a data mining-based prediction model capable of identifying the most effective promotional locations. The three classification algorithms used in this study are Logistic Regression, Support Vector Machine (SVM), and Decision Tree (C4.5). Student enrollment data were collected and processed through pre-processing stages, including missing value replacement, data normalization, and transformation of nominal attributes to numerical. The data were then split into training and testing subsets using a 70:30 split ratio. Evaluation results indicate that the Decision Tree (C4.5) model performed the best with an accuracy of 93.75%, precision of 97.37%, and recall of 90.24%. Logistic Regression also showed satisfactory results with an accuracy of 90.00%, precision of 92.31%, and recall of 87.80%. Meanwhile, SVM demonstrated lower performance with an accuracy of 72.50%, precision of 80.65%, and recall of 60.98%. In conclusion, the Decision Tree (C4.5) and Logistic Regression models are reliable for optimizing promotional strategies of higher education institutions, ensuring more efficient and effective resource allocation, and increasing new student enrollments. This study also makes a significant contribution to the literature related to the use of data mining for promotional strategies in the higher education sector.
Co-Authors Adenia Kusuma Dayanthi Anna Simatauw Antar Maramba Jawa Antonius Mbay Ndapamury Ardian Ariadi Ardito Laksono Suryoputro Arit Imanuel Meha Arvira Yuniar Isnaeni Ayuningtyas, Fajar Baali, Gabriel Megfaden Kenisa Baronio, Nodas Constantine Bintang Lazuardi Bistok Hasiholan Simanjuntak Brian Laurensz Brilliananta Radix Dewana Budhi Kristianto Bunga, Alex Frianco Cahyaningtyas, Christian Charitas Fibriani Christanto, Erwien Christiana Ari Setyaningrum Daniel HF Manongga Danny Manongga Devianto, Yudo Dian Widiyanto Chandra Dwi Hayati Edwin Zusrony Eko Sediyono Elvira Umar Engles Marabangkit Yoesmarlan Erik Wahyu Abdi Nugroho Evan Bagus Kristianto Evan Geraldy Suryoto Evi Maria Evi Maria Evi Maria Fabian Valerian Feibe Lawalata Florentina Tatrin Kurniati Gallen cakra adhi wibowo Gideon Bartolomeus Kaligis Gilbert Yesaya Likumahua Gudiato, Candra Haikal Nur Rachmanrachim Achaqie Haikal Nur Rachmanrachim Achaqie Hindriyanto Dwi Purnomo Ida Ayu Putu Sri Widnyani Indra Yunanto Irdha Yunianto Irwan Sembiring Isnaeni, Arvira Yuniar Joko Siswanto Joko Siswanto Josua Josen Alexander Limbong Kase, Celomitha Putri Welhelmina Kristoko Dwi Hartomo Kurnia Latifatul Nazila Laurentius Kuncoro Probo Saputra, Laurentius Kuncoro Probo Lobo, Murry Albert Agustin Lyonly Evany Tomasoa Maipauw, Musa Marsel Manongga, Daniel HF Maya Sari Merryana Lestari Mikhael Dio Eclesi Mila Chrismawati Paseleng Mira Mira Muhamad Yusup Muhammad Rizky Pribadi Muhammad Sholikhan Nadia Renatha Yuwono Nadya Inarossy Novem Berlian Uly Nugroho, Ignatius Dion Nurul Fadilah, Nurul Nusantara, Bandhu Otniel, Marcelinus Vito Patasik, Eva Sapan Patrick Simbolon Permatasari, Aurilia Dinda Petty, Holbed Joshua Praditya, Al-Farrel Raka Prayitno, Gunawan Priatna , Wowon Priyadi Priyadi Purwoko, Agus Qurotul Aini Ratu, Herman Huki Ravensca Matatula Raymond Elias Mauboy Riko Yudistira Rina Pratiwi Pudja I. A Rohmad Abidin, Rohmad Rony, Zahara Tussoleha Roy Rudolf Huizen Santoso, Nuke Puji Lestari Sarassati, Dwi Sinta Sebastian, Danny Septian Silvianugroho Septio, Pius Aldi Solly Aryza Sri Hartati Stanny Dewanty Rehatta Stevanus Dwi Istiavan Mau Supit, Christanti Ekkelsia Suryasatria Trihadaru Suryasatriya Trihandaru Susatyo, Yeremia Alfa Sutarto Wijono Theopillus J. H. Wellem Tirsa Ninia Lina Triloka Mahesti Triloka Mahesti Untung Rahardja Untung Rahardja Valentino Kevin Sitanayah Que Vinsensius Aprila Kore Dima Wahani, Puteri Justia Kardia Momuat Wasis Pancoro Wicaksono, Muhammad Ryqo Jallu Winarko, Edi Wiwin Sulistyo Yansen Bagas Christianto Yerik Afrianto Singgalen Yesi Arumsari Yohanes Aji Priambodo Yuliawan, Kristia