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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Informatics and Communication Technology (IJ-ICT) Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Journal of ICT Research and Applications International Journal of Advances in Intelligent Informatics CESS (Journal of Computer Engineering, System and Science) Proceeding of the Electrical Engineering Computer Science and Informatics Sistemasi: Jurnal Sistem Informasi Jurnal Teknologi dan Sistem Komputer Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research Knowledge Engineering and Data Science JIKO (Jurnal Informatika dan Komputer) International Journal of Computing and Informatics (IJCANDI) JURNAL REKAYASA TEKNOLOGI INFORMASI ILKOM Jurnal Ilmiah Prosiding SAKTI (Seminar Ilmu Komputer dan Teknologi Informasi) METIK JURNAL JISKa (Jurnal Informatika Sunan Kalijaga) Sains, Aplikasi, Komputasi dan Teknologi Informasi Indonesian Journal of Electrical Engineering and Computer Science JUKI : Jurnal Komputer dan Informatika Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences International Journal of Advanced Science and Computer Applications Adopsi Teknologi dan Sistem Informasi Bulletin of Social Informatics Theory and Application Periodicals of Occupational Safety and Health Pengabdian Kepada Masyarakat Bidang Teknologi dan Sistem Informasi The Indonesian Journal of Computer Science
Haviluddin Haviluddin
(SCOPUS ID: 56596793000, Universitas Mulawarman)
Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Public Health Social Sciences

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Data mapping process to handle semantic data problem on student grading system Arda Yunianta; Norazah Yusof; Arif Bramantoro; Haviluddin Haviluddin; Mohd Shahizan Othman; Nataniel Dengen
International Journal of Advances in Intelligent Informatics Vol 2, No 3 (2016): November 2016
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v2i3.84

Abstract

Many applications are developed on education domain. Information and data for each application are stored in distributed locations with different data representations on each database. This situation leads to heterogeneity at the level of integration data. Heterogeneity data may cause many problems. One major issue is about the semantic relationships data among applications on education domain, in which the learning data may have the same name but with a different meaning, or learning data that has a different name with same meaning. This paper discusses on semantic data mapping process to handle semantic relationships problem on education domain. There are two main parts in the semantic data mapping process. The first part is the semantic data mapping engine to produce data mapping language with turtle (.ttl) file format as a standard XML file schema, that can be used for Local Java Application using Jena Library and Triple Store. The Turtle file contains detail information about data schema of every application inside the database system. The second part is to provide D2R Server that can be accessed from outside environment using HTTP Protocol. This can be done using SPARQL Clients, Linked Data Clients (RDF Formats) and HTML Browser. To implement the semantic data process, this paper focuses on the student grading system in the learning environment of education domain. By following the proposed semantic data mapping process, the turtle file format is produced as a result of the first part of the process. Finally, this file is used to be combined and integrated with other turtle files in order to map and link with other data representation of other applications.
Comparing of ARIMA and RBFNN for short-term forecasting Haviluddin Haviluddin; Ahmad Jawahir
International Journal of Advances in Intelligent Informatics Vol 1, No 1 (2015): March 2015
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v1i1.10

Abstract

Based on a combination of an autoregressive integrated moving average (ARIMA) and a radial basis function neural network (RBFNN), a time-series forecasting model is proposed. The proposed model has examined using simulated time series data of tourist arrival to Indonesia recently published by BPS Indonesia. The results demonstrate that the proposed RBFNN is more competent in modelling and forecasting time series than an ARIMA model which is indicated by mean square error (MSE) values. Based on the results obtained, RBFNN model is recommended as an alternative to existing method because it has a simple structure and can produce reasonable forecasts.
Semantic data mapping technology to solve semantic data problem on heterogeneity aspect Arda Yunianta; Omar Mohammed Barukab; Norazah Yusof; Nataniel Dengen; Haviluddin Haviluddin; Mohd Shahizan Othman
International Journal of Advances in Intelligent Informatics Vol 3, No 3 (2017): November 2017
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v3i3.131

Abstract

The diversity of applications developed with different programming languages, application/data architectures, database systems and representation of data/information leads to heterogeneity issues. One of the problem challenges in the problem of heterogeneity is about heterogeneity data in term of semantic aspect. The semantic aspect is about data that has the same name with different meaning or data that has a different name with the same meaning. The semantic data mapping process is the best solution in the current days to solve semantic data problem. There are many semantic data mapping technologies that have been used in recent years. This research aims to compare and analyze existing semantic data mapping technology using five criteria’s. After comparative and analytical process, this research provides recommendations of appropriate semantic data mapping technology based on several criteria’s. Furthermore, at the end of this research we apply the recommended semantic data mapping technology to be implemented with the real data in the specific application. The result of this research is the semantic data mapping file that contains all data structures in the application data source. This semantic data mapping file can be used to map, share and integrate with other semantic data mapping from other applications and can also be used to integrate with the ontology language.
The Ontology-Based Methodology Phases To Develop Multi-Agent System (OmMAS) Arda Yunianta; Aina Musdholifah; Nataniel Dengen Haviluddin; Omar Obarukab Norazah Yusof; Herlina Jayadiyanti; Mohd Shahizan Othman
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 4: EECSI 2017
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (406.289 KB) | DOI: 10.11591/eecsi.v4.1027

Abstract

Semantic aspect on methodology phase is a significant  issue  to  develop  multi-agent  system in  the  current days.   There are a lot of methodologies to develop multi-agent system, but the current problem is how to choose the best methodology phase to develop current multi-agent system. The development of multi-agent system currently is to be more complex and difficult. Many aspects that contains on multi-agent system, the one of the famous issue now is about semantic aspect on multi-agent system. The old methodology phases are not suitable to develop current multi-agent system. Nowadays, many researchers start to improve and customize the obsolete methodology to adjust with the current needed. There are two research steps contains in this paper, the first step is to review and criticize previous methodologies especially about MOMA (Methodology for Developing Ontology-Based Multi-Agent System) was introduced in 2013. The second step is the main contribution of this paper is to improve previous methodology phases with the new methodology phases named OmMas (The Ontology-Based Methodology phases to Develop Multi-Agent System), and using semantic aspect as the  main focus of this methodology. The result of this research is improved ontology- based methodology phases as a representation of semantic aspect on the ontology development process. 
Prediksi Kedatangan Turis Asing ke Indonesia Menggunakan Backpropagation Neural Networks Haviluddin Haviluddin; Zainal Arifin; Awang Harsa Kridalaksana; Dedy Cahyadi
Jurnal Teknologi dan Sistem Komputer Volume 4, Issue 4, Year 2016 (October 2016)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (718.944 KB) | DOI: 10.14710/jtsiskom.4.4.2016.485-490

Abstract

In this paper, a backpropagation neural network (BPNN) method with time series data has been explored. The BPNN method to predict the foreign tourist’s arrival to Indonesia datasets have been implemented. The foreign tourist’s arrival datasets were taken from the central agency on statistics (BPS) Indonesia. The experimental results showed that the BPNN method with two hidden layers was able to forecast foreign tourist’s arrival to Indonesia. Where the mean square error (MSE) as forecasting accuracy has been indicated. In this study, the BPNN method is able and recommended to be alternative methods for predicting time series datasets. Also, the BPNN method showed that effective and easy to use. In other words, BPNN method is capable of producing a good value of forecasting.
Active Learning berbasis Teknologi Informasi (ICT) Haviluddin Haviluddin
Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer Vol 5, No 3 (2010): Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer
Publisher : Mulawarman University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (43.543 KB) | DOI: 10.30872/jim.v5i3.64

Abstract

Penerapan pembelajaran aktif di perguruan tinggi didasarkan pada prinsip bahwa cara belajar terbaik bagi mahasiswa adalah dengan melakukan, dengan menggunakan semua inderanya dan dengan mengeksplorasi lingkungannya yang terdiri atas orang, hal, tempat dan kejadian yang terjadi dalam kehidupan nyata (pembelajaran kontekstual dan pemecahan masalah). Untuk memfasilitasi pembelajaran aktif, dosen harus menggunakan berbagai strategi yang aktif dan kontekstual, melibatkan pembelajaran bersama (cooperative learning) dan mengakomodasi perbedaan jender dan gaya belajar masing-masing mahasiswa dengan tujuan untuk memaksimalkan kemampuan pembelajar dalam memahami hal baru dan dapat menggunakan informasi baru tersebut dalam kehidupan mereka sehari-hari. Teknologi informasi, meliputi segala hal yang berkaitan dengan proses, penggunaan sebagai alat bantu, manipulasi, dan pengelolaan informasi. Sementara teknologi komunikasi merupakan segala hal yang berkaitan dengan penggunaan alat bantu untuk memproses dan mentransfer data dari perangkat yang satu ke lainnya. Pembelajaran aktif sesungguhnya adalah bagaimana membuat proses pembelajaran itu lebih berpusat kepada mahasiswa yang aktif dengan memanfaatkan teknologi informasi atau ICT Component.
A Model of Non-ASN Employee Performance Assessment Based on the ROC and MOORA Methods Haviluddin Haviluddin; Edy Budiman; Nurfaizi Amin
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 2 (2022): April 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (378.723 KB) | DOI: 10.29207/resti.v6i2.3961

Abstract

This study aims to assess the performance of non-ASN employees at the Human Resources Development Agency (BPSDM), East Kalimantan Province, Indonesia, to assist organizers in determining the feasibility of extending work contracts. The performance of 37 non-ASN employees has been assessed based on 12 criteria, including honesty, discipline, loyalty, responsibility, courtesy, commitment, ability and skills, neatness, communication, achievement, absence, and violations. In this study, the Rank Order Centroid (ROC) and Multi-Objective Optimization based on Ratio Analysis (MOORA) methods have been implemented to obtain rankings. Meanwhile, the confusion matrix (CM) method has also been used to measure the accuracy of both methods. Based on the experiment, the ROC method has been used to achieve the criteria weight, and the MOORA method has been utilized to rank all non-ASN employees based on the highest score. Where the CM suitability level of 81.1% has been gained so that the ranking of 37 non-ASN employees can be revealed. The study indicates that both methods can be implemented as alternative models for assessing the performance of non-ASN employees. Therefore, these methods are pretty effective, efficient, and relatively easy to use.
Algoritma Backpropagation Neural Network dalam Memprediksi Harga Komoditi Tanaman Karet Julius Rinaldi Simanungkalit; Haviluddin Haviluddin; Herman Santoso Pakpahan; Novianti Puspitasari; Masna Wati
ILKOM Jurnal Ilmiah Vol 12, No 1 (2020)
Publisher : Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v12i1.521.32-38

Abstract

Rubber plantation sector is one of the leading commodities in East Kalimantan Province contributing greatly to non-oil and gas exports. Currently, the price of rubber in the world is increasingly competitive. The aim of this research is to predict the rubber prices as a reference for the government and companies in making policies and preparing work plans. Data of 60 months during the period of 2014-2018 taken from Plantation office of East Kalimantan Province has been analyzed using Backpropagation Neural Network (BPNN) algorithm in predicting rubber prices. Based on the testing results, parameters of the BPNN algorithm with ratio of 4: 1, architectural models 5-10-10-10-1, trainlm learning function, learning rate of 0.5, error tolerance of 0.01, and epoch of 1000 have gained good accuracy with a mean square error (MSE) of 0.00015464. The results showed that the BPNN algorithm can be used as an alternative method in forecasting.
PERAMALAN PELAYANAN SERVICE MOBIL (AFTER SALE) MENGGUNAKAN BACKPROPAGATION NEURAL NETWORK (BPNN) Novianti Puspitasari; Haviluddin Haviluddin; Arinda Mulawardani Kustiawan; Hario Jati Setyadi; Gubtha Mahendra Putra
JURNAL INFORMATIKA DAN KOMPUTER Vol 5, No 2 (2021): Februari 2021
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat - Universitas Teknologi Digital Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (414.567 KB) | DOI: 10.26798/jiko.v5i2.419

Abstract

Mobil adalah salah satu alat transportasi darat yang penting, karena mobil dapat membantu manusia dalam beaktivitas khususnya untuk pergi dari satu tempat ke tempat lain. Hal ini membuat para produsen mobil berlomba-lomba untuk menciptakan mobil dengan kelebihan dan keunggulan, sehingga jumlah mobil dipasaran sangat banyak dan bervariasi. Seiring dengan meningkatkan jumlah mobil maka Agen Tunggal Pemegang Merk (ATPM) berlomba-lomba untuk memberikan pelayanan after-sale (service mobile). Namun, pihak perusahaan mengalami kesulitan dalam mengetahui laju pertumbuhan jumlah service mobile yang ditangani, sehingga memberikan kerugian yang berdampak pada sumber pendapatan. Oleh karena itu diperlukan sebuah metode baku dalam menentukan peramalan jumlah service mobil di tahun berikutnya. Penelitian ini mengimplementasikan metode Backpropagation Neural Network (BPNN) dalam peramalan  pelayanan service mobil (after-sale) dan Mean Square Error (MSE) untuk metode pengujian akurasi hasil peramalan yang terbentuk. Adapun data yang digunakan pada penelitian ini adalah data  pelayanan service mobil (after-sale) selama lima tahun terakhir. Hasil penelitian menunjukkan bahwa arsitektur terbaik untuk peramalan pelayanan after-sale menggunakan BPNN adalah model arsitektur 5-10-5-1 dengan learning rate sebesar 0,2 dan fungsi pembelajaran yaitu trainlm serta MSE sebesar 0,00045581. Hal ini membuktikan bahwa metode BPNN mampu memprediksi pelayanan service mobile (after-sale) dengan nilai akurasi peramalan yang baik.
Implementation of Bandwidth Management Authentication Aulia Rahman; Haviluddin Haviluddin
International Journal of Computing and Informatics (IJCANDI) Vol 1, No 1 (2016): February 2016
Publisher : International Journal of Computing and Informatics (IJCANDI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1436.166 KB) | DOI: 10.19732/10.19732/vol1122016

Abstract

An internet traffic service mechanism includes monitoring and network security is indispensable. The main purpose of network monitoring is bandwidth optimizing and maintaining network security. This paper has described and implemented Remote Authentication Dial-In User Service (RADIUS) protocol and Authentication, Authorization and Accounting (AAA) server integrated with Mikrotik. The purpose of this article deal with the implementation of bandwidth management, which includes LAN (Local Area Network) and Wi-Fi (Wireless Fidelity) in Universitas Mulawarman. Based on experiment, the system is simple and easy to be used that controls and allocates bandwidth to users (lecturers, staff, and students) as they authenticate with LAN and Wi-Fi. Furthermore, network security perspective shows that users who are not registered to use the internet at the Universitas Mulawarman could be maintained as well.
Co-Authors Achmad Fanany Onnilita Gaffar Achmad Fanany Onnilita Gaffar Adnan, Adam Agus Soepriyadi Ahmad Hijazi, Mohd Hanafi Ahmad Jawahir Ahmad Jawahir Aiman, Ahmad Zuhair Nur Aina Musdholifah Aji Prasetya Wibawa Akhmad Masyudi Alfiansyah, M Nur Ali Sholihin Allo, Adriati Manuk Anam, M Khairul Anggari, Ricky Anindita Septiarini, Anindita Anton Prafanto Arda Yunianta Arda Yunianta Arif Bramantoro Arif Harjanto Arinda Mulawardani Kustiawan Astuti, Wistiani Aulia Rahman Awang Harsa Kridalaksana Bambang Nur Basuki Bangkit Bekti Nurdianto Basuki, Nur Bambang Brins Leonard Pailan Budiman, Edy Burhandenny, Aji Ery Cahyani, Oktari Indi Cholisah Erman Hasihi Chrisman Bonor Sinaga Darwis, Herdianti Davina Putri Ananta Dedy Cahyadi Dedy Mirwansyah Delvina Dwiani Samjar Dhanar Intan Surya Saputra Dhanar Intan Surya Saputra Didit Suprihanto, Didit Dinda Izmya Nurpadillah Djoko Setyadi Dwiyanto, Felix Andika Efrizoni, Lusiana Fahrul Agus Fatkhul Hani Rumawan Fauzan, Ammar Nabil Faza Alameka Fazma Urmila Jannah Helmi Puadi Fengchang, Xu Firdaus, Ardhifa Firdaus, Muhammad Bambang Fui Fui, Ching Fui, Ching Fui Gaffar, Emmilya Umma Aziza Gubtha Mahendra Putra Gubtha Mahendra Putra Gultom, Tiopan Hendry Manto Guozhang, Li Hairah, Ummul Hamdani Hamdani Hatta, Heliza Rahmania Heliza Rahmania Hatta, Heliza Rahmania Helmi Puadi, Fazma Urmila Jannah Herdianti Darwis Herlina Jayadiyanti Herman Santoso Pakpahan Hery Widijanto Hijazi, Mohd Hanafi Ahmad Hijratul Aini Hijratul Aini Huzain Azis Ibrahim, Muhammad Rivani Ifandi, Muhammad Imam Tahyudin Imam Tahyudin Irwan Gani Islamiyah Islamiyah Islamiyah Islamiyah Iwan Muhamad Ramdan Izdihar, Zahra Nabila Jainuddin Jainuddin Jayadiyanti, Herlina Julius Rinaldi Simanungkalit Kesuma, Muhammad Afrizal Kim On, Chin Leong, Jing Mei Lilik Hendrajaya Malani, Rheo Maratus Soleha Masyudi, Akhmad Mega Yoalifa Ming Foey Teng Mohd Shahizan Othman Mohd Shahizan Othman Mualin Renaldy Setiabudi Muhammad Bambang Muhammad Rafif Hanif Muhammad Soleh Muhammad Sultan, Muhammad Muhammad Syarif Abdillah Nafalski, Andrew Nataniel Dengen Ngurah Satria Darmawangsa Ni’mah Moham Norazah Yusof Novianti Puspitasari Nugraha, Cellia Auzia Nugroho, Basuki Rahmat Nurfaizi Amin Olivia Angelica Murtioso Omar Mohammed Barukab Omar Obarukab Norazah Yusof Othman, Mohd Shahizan Pailus, Rayner Paroliyan, Abraham Pradinata, Muhammad Aji Prafanto, Anton Pratama, Arief Ardi Prawira, Muhammad Nanda Purnawansyah Purnawansyah Puspitasari, Novianti Putut Pamilih Widagdo, Putut Pamilih Qonita, Adiba Rahayu, Ervina Raja, Roesman Ridwan Rayner Alfred Rayner Alfred Rayner Alfred Rayner Alfred Rayner Alfred Rayner Alfred Rendy Ramadhan Rima Yustika Hasnida Saputra, Irzan Tri Sarjon Defit Saudi, Azali Setyadi, Hario Jati Simanungkalit, Julius Rinaldi Sitompul, Tua Delima Soepriyadi, Agus Suryani Junita Patandianan Sutikno Sutikno Suwardi Gunawan Taruk, Medi Tindik, Emmanuel Steward Triyanna Widiyaningtyas Triyanna Widyaningtyas Triyanna Widyaningtyas, Triyanna Utama, Agung Bella Putra Utomo Pujianto Vina Zahrotun Kamila Wandi, Faizul Anwar Wati, Masna Wei, Toh Yin Widians, Joan Angelina Wong, Kelvin Yahya, Fiqri Khaidar Yudi Sukmono, Yudi Yulita Salim Yunianta, Arda Yusof, Omar Obarukab Norazah Zainal Arifin Zainal Arifin