Article Per Year (5 Year)
p-Index From 2020 - 2025
0.882
P-Index
This Author published in this journals
All Journal Jurnal INKOM Bulletin of Electrical Engineering and Informatics JOIN (Jurnal Online Informatika) Jurnal Teknologi dan Sistem Komputer Teknologi Indonesia Journal of Artificial Intelligence and Digital Business
Andria Arisal, Andria
Unknown Affiliation
Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering

Published : 10 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 10 Documents
Search

 1 
DISTRIBUTED CONSTRAINTS SATISFACTION FOR MULTI AGENT SYSTEM Arisal, Andria
Teknologi Indonesia Vol 33, No 2 (2010)
Publisher : LIPI Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14203/jti.v33i2.27

Abstract

Multi agent system usually has to solve problems with some specifi c constraints, which are possibly to be over constraint. Moreover multiagent system has to utilize existing agents with their limited constraints knowledge. To address this problem, we use one distributed constraint satisfaction problem algorithm, asynchronous incremental relaxation in extreme kitchen domain. This algorithm uses multivalue backtrack with threshold value has proven able to solve over constraints problem by suggesting goal with the least constraint violation.
Analisa keterkaitan (link analysis) dengan menggunakan Sequential Pattern Discovery untuk prediksi cuaca Suwarningsih, Wiwin; Nuryani, Nuryani; Arisal, Andria
INKOM Journal Vol 4, No 1 (2010)
Publisher : Pusat Penelitian Informatika - LIPI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (91.922 KB) | DOI: 10.14203/j.inkom.75

Abstract

Tujuan penelitian ini adalah menganalisa keterkaitan antar atribut/itemset pada parameter yang digunakan dalam prediksi cuaca untuk menghasilkan suatu aturan(rule) yang dapat membuktikan suatu kondisi apakah hujan atau tidak hujan. Metoda yang akan digunakan untuk analisa antar atribut ini adalah Sequential Pattern Mining, dimana pola kerjanya adalah menganalisa keterkaitan suatu atribut akan mempengaruhi atribut lainnya dan bagaimana ketergantungan atribut yang satu dengan atribut lainnya. Hasil akhir dari penelitian ini adalah menemukan pola-pola pengetahuan yang tersembunyi di dalam data. Pola tersebut berbentuk aturan (rule) yang dapat membantu menentukan kondisi cuaca apakah hari ini hujan atau tidak.
IGNITER: Membangun Komputer Cluster Sederhana dengan Cepat Arisal, Andria; Suwarningsih, Wiwin; Nuryani, Nuryani
INKOM Journal Vol 4, No 2 (2010)
Publisher : Pusat Penelitian Informatika - LIPI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (60.26 KB) | DOI: 10.14203/j.inkom.52

Abstract

Karena kesulitan dalam memperoleh akses ke fasilitas infrastrukutur komputer cluster pada waktu diperlukan, dan kesulitan dalam mengumpulkan paket installer sewaktu akan membangun infrastruktur komputer cluster sendiri, maka dikembangkanlah suatu installer sistem operasi linux yang dapat membuat infrastruktur komputer cluster sederhana dengan cepat. Dengan mengacu kepada pemaketan dan installasi distribusi linux Fedora dan IGN, kami mengembangkan linux-live dalam bentuk CD dan USB, yang disebut dengan IGNITER (IGOS Nusantara for Instant Cluster). IGNITER dapat mempercepat pengembangan infrastruktur cluster sederhana dalam 5 langkah konfigurasi.Kata kunci: komputer cluster, linux-live, installer, igniter, fedora, ign
IGN Server untuk Implementasi e-Government Masthurah, Nurhayati; Ni'mah, Iftitahu; Arisal, Andria; Evandri, Evandri; Yuwana, Sandra
INKOM Journal Vol 6, No 1 (2012)
Publisher : Pusat Penelitian Informatika - LIPI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (624.815 KB) | DOI: 10.14203/j.inkom.150

Abstract

Tulisan ini bertujuan memaparkan mengenai pengembangan IGN Server berbasis system operasi IGOS Nusantara yang dilengkapi dengan aplikasi-aplikasi pendukung e-Government. Sistem operasi open source IGN server berasal dari IGN Desktop yang telah dikembangkan beberapa tahun sebelumnya dalam upaya memenuhi kebutuhan server yang open source dan terjangkau. Hasil dari penelitian ini adalah satu paket IGN server dengan aplikasi pendukung e-Government yaitu Moodle, SpagoBI, FengOffice, dan Project Senayan. Diharapkan dengan beberapa paket aplikasi yang diberikan dapat memacu beberapa pegawai di instansi pemerintahan dalam menggunakan produk open source.
Rekayasa Keamanan pada Kompilator Online untuk Pemrograman Paralel Arisal, Andria; Wirahman, Taufiq; Nuryani, Nuryani; Suwarningsih, Wiwin
INKOM Journal Vol 3, No 1-2 (2009)
Publisher : Pusat Penelitian Informatika - LIPI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (61.27 KB) | DOI: 10.14203/j.inkom.42

Abstract

Kami mengembangkan kompilator online untuk pemrogram parallel sebagai suatu kakas pembelajaran untuk mempraktekkan dan memahami paradigm pemrograman paralel. Kakas ini memiliki antarmuka langsung dengan pengguna umum melalui Internet, sehingga selain mudah digunakan, akan sangat rentan terhadap berbagai macam serangan online. Kami menyadari bahwa factor keamanan adalah salah satu aspek penting dari pengembangan perangkat lunak yang harus diterapkan pada pengembangan kompilator online tersebut. Dengan menganalisis kelamahan dan serangan yang mungkin terjadi, kompilator online ini dibangun dengan menggunakan dua tahapan pengamanan. Pengamanan tahap pertama adalah sebagai bagian dari aplikasi web yang merupakan antarmuka pengamanan terhadap aplikasi web. Pengamanan kedua adalah pengamanan untuk melindungi cluster computer sebagai sumber daya yang digunakan untuk mengeksekusi aplikasi yang dibuat dengan paradigm pemrograman parallel. Kedua lapisan pengamanan ini dianggap cukup dapat mengamankan aplikasi dan sumber daya dari pengguna atau kode yang tidak diharapkan.Kata kunci: kompilator online, keamanan, kerentanan, serangan, cluster, aplikasi web, pemrograman paralel
Automatic detection of crop diseases using gamma transformation for feature learning with a deep convolutional autoencoder Zilvan, Vicky; Ramdan, Ade; Supianto, Ahmad Afif; Heryana, Ana; Arisal, Andria; Yuliani, Asri Rizki; Krisnandi, Dikdik; Suryawati, Endang; Suryo Kusumo, Raden Budiarianto; Yuawana, Raden Sandra; Kadar, Jimmy Abdel; Pardede, Hilman F.
Jurnal Teknologi dan Sistem Komputer [IN PRESS] Volume 10, Issue 3, Year 2022 (July 2022)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jtsiskom.2022.14250

Abstract

Precision agriculture is a management strategy for sustaining and increasing the production of agricultural commodities. One of its implementations is for crop disease detection. Currently, deep learning methods have become widespread methods for the automatic detection of crop diseases. Most deep learning methods showed better performance when using an original image in raw form as inputs. However, the original image of crop diseases may appear similar between one disease to another.  Therefore, the deep learning methods may misclassify the data. To deal with these, we propose the gamma transformation with a deep convolutional autoencoder to extract good features from the original image data. We use the output of the gamma transformation with a deep convolutional autoencoder as inputs to a classifier for the automatic detection of crop diseases. Our experiments show that the average accuracies of our method improve the performance of crop disease detection compared to only using raw data as inputs.
Analysis of alternatives methodology for large-scale information system implementation Arisal, Andria; Setiadi, Bambang; Muslim, Ichwanul
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.7800

Abstract

According to the Presidential Decree, central and local governments must implement electronic-based government systems or sistem pemerintahan berbasis elektronik (SPBE). However, the independent implementations have created various similar applications to support the same field of governmental activities. The situation creates difficulties in achieving effectiveness, integration, sustainability, efficiency, accountability, interoperability, and security of governmental services. Therefore, a common application will be developed for each governmental activity to improve interoperability and data integration. On the other hand, central or local governments must consider the suitable implementation of their public service information systems. This manuscript guides the determination of alternatives using cost, benefit, and risk analysis. We use the proposed guidance for a case study because sistem pengelolaan pengaduan pelayanan publik nasional-layanan aspirasi dan pengaduan online rakyat (SP4N-LAPOR!) has been regulated as the common application for Public Service Complaints Management using PermenPANRB No. 680, 2020. The application of the proposed guidance shows that it can help the stakeholder quantitatively decide on an alternative implementation of the application for the public service complaints management system.
Chili leaf segmentation using meta-learning for improved model accuracy Suwarningsih, Wiwin; Kirana, Rinda; Husnul Khotimah, Purnomo; Riswantini, Dianadewi; Fachrur Rozie, Andri; Nugraheni, Ekasari; Munandar, Devi; Arisal, Andria; Roufiq Ahmadi, Noor
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.7929

Abstract

Recognizing chili plant varieties through chili leaf image samples automatically at low costs represents an intriguing area of study. While maintaining and protecting the quality of chili plants is a priority, classifying leaf images captured randomly requires considerable effort. The quality of the captured leaf images significantly impacts the development of the model. This study applies a meta-learning approach to chili leaf image data, creating a dataset and classifying leaf images captured using mobile devices with varying camera specifications. The images were organized into 14 experimental groups to assess accuracy. The approach included 2-way and 3-way classification tasks, with 3-shot, 5-shot, and 10-shot learning scenarios, to analyze the influence of various chili leaf image factors and optimize the classification and segmentation model's accuracy. The findings demonstrate that a minimum of 10 shots from the meta-test dataset is sufficient to achieve an accuracy of 84.87% using 2-way classification meta-learning combined with the mix-up augmentation technique.
Performance Evaluation of NAS Parallel and High-Performance Conjugate Gradient Benchmarks in Mahameru Wirahman, Taufiq; Latifah, Arnida L; Muttaqien, Furqon Hensan; Swardiana, I Wayan Aditya; Arisal, Andria; Iryanto, Syam Budi; Sadikin, Rifki
JOIN (Jurnal Online Informatika) Vol 10 No 2 (2025)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v10i2.1557

Abstract

High-Performance Computing (HPC) plays a crucial role in accelerating scientific advancement across numerous fields of research and in effectively implementing various complex scientific applications. Mahameru is one of the largest national HPC systems in Indonesia and has been utilized by many sectors. However, it has not undergone proper benchmarking evaluation, which is vital for identifying issues related to hardware and software configurations and confirming system reliability. Therefore, this study aims to evaluate the performance, efficiency, and capabilities of Mahameru. We present a benchmarking system on Mahameru utilizing two benchmark suites: the NAS Parallel Benchmarks (NPB) and the high-performance conjugate gradient (HPCG) benchmark. Our results indicate that the NPB exhibits a lower speedup in Message Passing Interface (MPI) compared to OpenMP, which can be attributed to the communication overhead and the nature of the computational tasks. Additionally, the HPCG benchmark demonstrates that Mahameru performance can compete with the lower tiers of the Top 500 supercomputers. When operating at full capacity, Mahameru can achieve approximately 2.5% of its theoretical peak performance. While the system generally performs reliably with parallel algorithms, it may not fully leverage hyperthreading with certain algorithms. This benchmark result can serve as a basis for decision-making regarding potential upgrades or changes to a system.
Analisis Prediksi Harga Sewa Ruko Menggunakan Pendekatan Machine Learning Munawar, Yunadi Fitra; Arisal, Andria
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 4 No. 3 (2025): Agustus - October
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v4i3.2184

Abstract

Penelitian ini mengembangkan sistem prediksi harga sewa ruko menggunakan pendekatan machine learning di tiga kota besar di Indonesia: Jakarta, Semarang, dan Surabaya. Ruko merupakan komponen penting dalam pasar properti komersial di Indonesia. Penentuan harga sewa yang akurat sangat dibutuhkan oleh pemilik properti, investor, maupun pemerintah daerah. Penelitian ini menggunakan tiga algoritma machine learning: Random Forest, Support Vector Regression (SVR), dan XGBoost. Data diperoleh dari hasil web scraping situs properti online dan diperkaya dengan variabel tambahan seperti kepadatan penduduk. Evaluasi dilakukan menggunakan MAE, MAPE, RMSE, dan R² Score. Hasilnya, SVR menunjukkan kinerja terbaik di Semarang dan Surabaya, sementara XGBoost unggul di Jakarta. Agar dapat digunakan secara luas, model terbaik diintegrasikan ke dalam aplikasi web sederhana berbasis Streamlit. Pengguna cukup memasukkan detail properti, dan sistem akan memberikan estimasi harga sewa secara langsung. Aplikasi ini memberikan kemudahan dalam penilaian harga sewa yang cepat dan objektif, serta mendukung pengambilan keputusan berbasis data di sektor properti Indonesia.
Co-Authors Ade Ramdan Ahmad Afif Supianto Bambang Setiadi Devi Munandar, Devi Evandri, Evandri Fachrur Rozie, Andri Furqon Hensan Muttaqien Heryana, Ana Husnul Khotimah, Purnomo Kadar, Jimmy Abdel Krisnandi, Dikdik Latifah, Arnida L Masthurah, Nurhayati Munawar, Yunadi Fitra Muslim, Ichwanul Ni'mah, Iftitahu Nugraheni, Ekasari Nuryani Nuryani Pardede, Hilman F. Rinda Kirana Riswantini, Dianadewi Roufiq Ahmadi, Noor Sadikin, Rifki Suryawati, Endang Suryo Kusumo, Raden Budiarianto Swardiana, I Wayan Aditya Syam Budi Iryanto, Syam Budi Vicky Zilvan, Vicky Wirahman, Taufiq Wiwin Suwarningsih Yuawana, Raden Sandra Yuliani, Asri Rizki Yuwana, Sandra