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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Format : Jurnal Imiah Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Computatio : Journal of Computer Science and Information Systems International Journal of Supply Chain Management Jurnal Sosial Ekonomi Kelautan dan Perikanan Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika EKOLOGIA : Jurnal Ilmiah Ilmu Dasar dan Lingkungan Hidup Journal of Applied Sciences and Advanced Technology Jurnal Informatika dan Rekayasa Perangkat Lunak
Fajar Delli Wihartiko
Universitas Pakuan
Agriculture, Biological Sciences & Forestry Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Mathematics Social Sciences Transportation

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PENDUGAAN PARAMETER MODEL PENINGKATAN POPULASI PEROKOK DENGAN METODE DEKOMPOSISI ADOMIAN MULTISTAGE Hagni Wijayanti; Fajar Delli Wihartiko
Ekologia: Jurnal Ilmiah Ilmu Dasar dan Lingkungan Hidup Vol 14, No 2 (2014): Ekologia : Jurnal Ilmiah Ilmu Dasar dan Lingkungan Hidup
Publisher : Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33751/ekol.v14i2.214

Abstract

The prevalence of smoking in Indonesia over the years tend to experience increased. Increasing cigarette consumption in Indonesia will affect public health conditions and demanding high fees from the public, so that the necessary control of the consumption of cigarettes wisely. The smoking prevalence of the problem, can be made a mathematical formulation. There are three parameters involved, namely the active smoker population, the population of smokers and potential smokers who've stopped population, along with the change in time. Identify the parameter is very important, because it can show how the problem of the prevalence of smokers. In this study used method of Multistage Adomian Decomposition (MADM) which gives the solution of the model population increase smokers as solution series t (time) for each subinterval m during the period from [0, T]. The resolution of the model and the simulation model do with programming in Mathematica version 8. Key words : method of Multistage Adomian Decomposition (MADM),  prevalence of smokers
Comparison of Genetic Algorithm Optimization with Support Vector Machine (SVM) for Weather Forecast Andriani, Siska; Wihartiko, Fajar Delli
Journal of Applied Sciences and Advanced Technology Vol. 6 No. 3 (2024): Journal of Applied Sciences and Advanced Technology
Publisher : Faculty of Engineering Universitas Muhammadiyah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24853/jasat.6.3.83-90

Abstract

Weather forecasts are one of the important factors for daily activities. It can be used for daily work activities such as farming, aviation, production and distribution. The Meteorology, Climatology and Geophysics Agency makes weather forecasts based on weather parameters, namely temperature, air pressure, solar radiation, humidity and rainfall. The weather forecast class is divided into 5 classes, namely Cloudy, Rainy, Sunny, Cloudy Rainy and Cloudy Sunny. In this research, a comparison of weather forecast models using Learning Vector Quantization optimization and Genetic Algorithms will be made with weather forecast models using the Support Vector Machine method. The data used in this research is weather data at the Citeko Class III Climatology and Geophysics station, the data used is data from the last 3 years. Then the data is divided into training and test data using percentage split with a division of 65% used for training data and 35% used for test data. After making the model using the LVQ-GA and SVM methods, a comparison of the model test results was carried out, from the test results the accuracy value was calculated using a confusion matrix for each model. The accuracy result of the LVQ-GA optimization weather forecast model was 73%, while the weather forecast model using the SVM method obtained an accuracy value of 81.5%, thus the results from SVM were better.
Clean Water Demand Prediction Model Using The Long Short Term Memory (LSTM) Method Sari, Delviani Permata; Karlitasari, Lita; Wihartiko, Fajar Delli
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol 20, No 2 (2023): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika
Publisher : Ilmu Komputer, FMIPA, Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33751/komputasi.v20i2.8060

Abstract

Cities or districts as population centers with various service facilities, really need the provision of clean water. The agency that handles clean water in Indonesia is the Regional Drinking Water Company (PDAM). PDAMs were established in every city and district in Indonesia as agencies that serve the community's need for clean water. One of them is the Regional Public Company (Perumda) Tirta Pakuan and as time goes by the number of customers will definitely increase so that the need for clean water will also increase. The purpose of this research is to create a Clean Water Demand Prediction Model using the Long Short Term Memory (LSTM) Method to find the most optimal modeling. The data in this study were obtained from data reports is from Perumda Tirta Pakuan. The prediction model development process is carried out through Visual Studio Code tools. To find a model with the smallest error rate using various ratios, namely 80:20, 70:30, 60:40, and 50:50, then testing is also carried out based on the number of different hyperparameter values in batch sizes 5, 10, 15, 20, 25 and max epoch 50, 100, 150, 200, 250. From all the experiments that have been carried out, the most optimal is batch size 5 and epoch 50 with a ratio of 60:40 for water production to get RMSE 0.4862 and MAPE 2.5252% while for the amount of water use with a ratio of 50:50 get RMSE 0.4674 and MAPE of 2.5163%.
Blockchain dan Kecerdasan Buatan dalam Pertanian : Studi Literatur Wihartiko, Fajar Delli; Nurdiati, Sri; Buono, Agus; Santosa, Edi
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 8 No 1: Februari 2021
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.0814059

Abstract

Dewasa ini teknologi blockchain dan kecerdasan buatan (artificial intelligence/AI) telah diimplementasikan dalam bidang pertanian. Teknologi blockchain menjanjikan keamanan dan peningkatan kepercayaan untuk pengguna. Teknologi kecerdasan buatan menjanjikan berbagai kemudahan bagi pengguna. Perpaduan kedua teknologi tersebut dapat meningkatan kepercayaan terhadap sistem kecerdasan buatan (blockchain for AI) atau dapat juga digunakan untuk meningkatkan kinerja sistem blockchain (AI for blockchain). Tujuan penelitian ini mengulas kedua teknologi tersebut dalam studi literatur serta memberikan tantangan riset ke depan terkait implementasinya di bidang pertanian.  Metodologi yang digunakan adalah Systematic Literature Review (SLR) dan text mining. Text mining digunakan untuk memberikan deskripsi riset yang ada berdasarkan kata-kata di setiap artikel terpilih. SLR digunakan untuk memberikan ulasan yang komprehensif terkait riset Blockchain dan kecerdasan Buatan dalam pertanian. Hasil penelitian menunjukan bahwa terdapat 10 % penelitian terkait penerapan blockchain dan AI dalam pertanian. Riset tersebut memiliki potensi besar untuk berkembang terlihat dari peningkatan jumlah publikasi dalam 2 tahun terakhir. Kontribusi penelitian ini meliputi posisi riset terkini dan usulan riset ke depan dengan mempertimbangkan kondisi pertanian Indonesia. Posisi riset tersebut didominasi komunitas peneliti dari negara-negara di Asia seperti India (33%), Pakistan (33%), China (14%) dan Korea (14%). Originalitas penelitian ini terletak pada studi literatur dari integrasi teknologi blockchain dan kecerdasan buatan dalam bidang pertanian menggunakan SLR dan text mining. AbstractArtificial intelligence and blockchain technology are being developed and implemented in Agriculture. Blockchain technology promises security and trust for users. Moreover, artificial intelligence technology promises convenience for users. The combination of these two technologies will increase trust in artificial intelligence systems. Besides, this combination can also increase security on the blockchain system through the application of artificial intelligence. This paper summarizes the application of both technologies and reviews them in a systematic literature review, presents a description of articles based on text mining, and provides future research challenges related to the implementation of blockchain and artificial intelligence in agriculture. The methodologies used are Systematic Literature Review (SLR) and text mining. Text mining is used to describe a description of existing research based on the words in each selected article. SLR is used to provide a comprehensive review of Blockchain research and Artificial intelligence in agriculture. The results showed that there were 10% of research related to the application of blockchain and AI in agriculture. This research has great potential for growth as seen from the increase in the number of publications in the last 2 years. The contribution of this research includes the latest research positions and future research proposals taking into account the conditions of Indonesian agriculture. The research position is dominated by the research community from countries in Asia such as India (33%), Pakistan (33%), China (14%) and Korea (14%). The originality of this research is a literature study on the integration of blockchain and artificial intelligence in agriculture using SLR and text mining.
PEMODELAN MONTE CARLO UNTUK PREDIKSI SIFAT HUJAN HARIAN Andriani, Siska; Akhmad, Dinar Munggaran; Wihartiko, Fajar Delli
Computatio : Journal of Computer Science and Information Systems Vol. 4 No. 2 (2020): 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.v4i2.9697

Abstract

Prediksi merupakan kegiatan peramalan untuk masa depan. Prediksi sudah banyak digunakan salah satunya untuk prediksi panen, jumlah keuntungan dan kerugian serta prediksi cuaca. Pada penelitian ini akan memanfaatkan prediksi cuaca untuk mengetahui sifat hujan harian menggunakan model monte carlo. Dalam prediksi hujan harian ada beberapa parameter yang dapat mempengaruhi missalnya temperatur, curah hujan, kelembaban, arah angin, tekanan udara dan lain sebagainya. Pengamatan cuaca dilakukan oleh Badan Meteorologi Klimatologi dan Geofisika (BMKG). Data pengamatan yang digunakan pada penelitian ini adalah data pengamatan yang dilakukan stasiun BMKG Waingapu selama 40 tahun (1973-2013). BMKG dalam melakukan prediksi masih sering menemukan kendala karena iklim cuaca di Indonesia dirasa masih sangat labil, sehingga hasil akurasi prediksi sangat sulit dilakukan dengan menggunakan cara tradisional. Untuk itu diusulkan prediksi sifat hujan harian dengan pembangunan model sifat hujan harian menggunakan metode monte carlo. Tahapan metode yang dilakukan dimulai dari analisis, perancangan, implementasi dan uji validasi. Pada tahap implementasi dilakukan pemodelan dimana tahap awal yaitu melakukan analisis data cuaca, penentuan Awal Musim Hujan (AMH), analisis korelasi antara data AMH dengan data Anomali Suhu Permukaan Laut (ASPL) Nino 3.4, penentuan 3 kelas data menggunakan SOM, kategorisasi 9 sifat hujan harian, pemodelan dengan metode monte carlo dan uji coba validasi.
Advancing Smart City Infrastructure: A Deep Learning-Based Framework for Real-Time Traffic Monitoring and Violation Detection Using YOLOv11 Gustian Rama Putra; Adrian Jaleco Forca; Reiner Jun Gepayo Alminaza; Fajar Delli Wihartiko; Raid Rafif
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 7 No. 1 (2026): Volume 7 Number 1 March 2026
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jatika.v7i1.1354

Abstract

Urban traffic congestion and violations of dedicated bus lanes in metropolitan cities, such as Jakarta, are significant challenges affecting the efficiency of public transportation systems. Traditional traffic monitoring methods are insufficient to address these issues, particularly in real-time violation detection. This research proposes an AI-based smart traffic monitoring framework using YOLOv11 for real-time detection of vehicle violations in TransJakarta’s Bus Rapid Transit (BRT) lanes. The study aims to improve urban mobility by enhancing the detection accuracy and speed of traffic monitoring systems. The methodology involves data collection from surveillance cameras, data annotation using Roboflow, and model training with YOLOv11, utilizing transfer learning and hyperparameter optimization. The system's performance is evaluated through precision, recall, F1-score, and mean Average Precision (mAP@0.5), as well as real-time inference speed. The results show that YOLOv11 achieves a mAP@0.5 of 0.946 and an F1-score of 0.898, demonstrating the model's high accuracy in detecting vehicle violations across different lighting conditions. Real-time inference is achieved at a rate of 35-40 FPS, making it suitable for deployment in real-world urban environments. This research concludes that the YOLOv11-based framework is an effective solution for automated traffic monitoring, offering significant implications for smart city development and intelligent transportation systems. Further research is needed to address lighting challenges and improve the system's scalability across various urban settings.
Co-Authors Adrian Jaleco Forca Agus Buono Dinar Munggaran Akhmad Dinar Munggaran Akhmad, Dinar Munggaran Edi Santosa Eneng Tita Tosida Feriadi Feriadi, Feriadi Fitria Virgantari Fredi Andria Freshty Yulia Arthatiani Gustian Rama Putra Hagni Wijayanti Hermawan Hermawan Hoerudin, Andi Irfan Wahyudin Irman Hermadi Lita Karlitasari Lola Jaman Sentosa Raid Rafif Reiner Jun Gepayo Alminaza Sari, Delviani Permata Siska Andriani Siska Andriani Sonny Koeshendrajana Sri Nurdiati Utep Utep Yani Nurhadryani Yasmin Erika Faridhan Yugo Ahmad Abdillah