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All Journal International Journal of Electrical and Computer Engineering Perfecting a Video Game with Game Metrics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Annual Research Seminar Proceeding of the Electrical Engineering Computer Science and Informatics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Teknologi Terpadu Jurnal Generic
Reza Firsandaya Malik
Universitas Sriwijaya
Computer Science & IT Electrical & Electronics Engineering Engineering

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Credit Scoring Using Classification and Regression Tree (CART) Algorithm and Binary Particle Swarm Optimization Reza Firsandaya Malik; Hermawan Hermawan
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 6: December 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (469.545 KB) | DOI: 10.11591/ijece.v8i6.pp5425-5431

Abstract

Credit scoring is a procedure that exists in every financial institution. A way to predict whether the debtor was qualified to be given the loan or not and has been a major concern in the overall steps of the loan process. Almost all banks and other financial institutions have their own credit scoring methods. Nowadays, data mining approach has been accepted to be one of the well-known methods. Certainly, accuracy was also a major issue in this approach. This research proposed a hybrid method using CART algorithm and Binary Particle Swarm Optimization. Performance indicators that are used in this research are classification accuracy, error rate, sensitivity, specificity, and precision. Experimental results based on the public dataset showed that the proposed method accuracy is 78 %. In compare to several popular algorithms, such as neural network, logistic regression and support vector machine, the proposed method showed an outstanding performance. 
Bigram feature extraction and conditional random fields model to improve text classification clinical trial document Jasmir Jasmir; Siti Nurmaini; Reza Firsandaya Malik; Bambang Tutuko
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 3: June 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i3.18357

Abstract

In the field of health and medicine, there is a very important term known as clinical trials. Clinical trials are a type of activity that studies how the safest way to treat patients is. These clinical trials are usually written in unstructured free text which requires translation from a computer. The aim of this paper is to classify the texts of cancer clinical trial documents consisting of unstructured free texts taken from cancer clinical trial protocols. The proposed algorithm is conditional random Fields and bigram features. A new classification model from the cancer clinical trial document text is proposed to compete with other methods in terms of precision, recall, and f-1 score. The results of this study are better than the previous results, namely 88.07 precision, 88.05 recall and f-1 score 88.06.
The New Multipoint Relays Selection in OLSR using Particle Swarm Optimization Reza Firsandaya Malik; Tharek Abdul Rahman; Razali Ngah; Siti Zaiton Mohd; Hashim Hashim
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 10, No 2: June 2012
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v10i2.804

Abstract

The standard optimized link state routing (OLSR) introduces an interesting concept, the multipoint relays (MPRs), to mitigate message overhead during the flooding process. This paper propose a new algorithm for MPRs selection to enhance the performance of OLSR using particle swarm optimization sigmoid increasing inertia weight (PSOSIIW). The sigmoid increasing inertia weight has significance improve the particle swarm optimization (PSO) in terms of simplicity and quick convergence towards optimum solution. The new fitness function of PSOSIIW, packet delay of each node and degree of willingness are introduced to support MPRs selection in OLSR. The throughput, packet loss and end-to-end delay of the proposed method are examined using network simulator 2 (ns2).  Overall results indicate that OLSR-PSOSIIW has shown good performance compared to the standard OLSR and OLSR-PSO, particularly for the throughput and end-to-end delay. Generally the proposed OLSR-PSOSIIW shows advantage of using PSO for optimizing routing paths in the MPRs selection algorithm.
Neural network technique with deep structure for improving author homonym and synonym classification in digital libraries Firdaus Firdaus; Siti Nurmaini; Varindo Ockta Keneddi Putra; Annisa Darmawahyuni; Reza Firsandaya Malik; Muhammad Naufal Rachmatullah; Andre Herviant Juliano; Tio Artha Nugraha
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 4: August 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i4.18878

Abstract

Author name disambiguation (AND), also recognized as name-identification, has long been seen as a challenging issue in bibliographic data. In other words, the same author may appear under separate names, synonyms, or distinct authors may have similar to those referred to as homonyms. Some previous research has proposed AND problem. To the best of our knowledge, no study discussed specifically synonym and homonym, whereas such cases are the core in AND topic. This paper presents the classification of non-homonym-synonym, homonym-synonym, synonym, and homonym cases by using the DBLP computer science bibliography dataset. Based on the DBLP raw data, the classification process is proposed by using deep neural networks (DNNs). In the classification process, the DBLP raw data divided into five features, including name, author, title, venue, and year. Twelve scenarios are designed with a different structure to validate and select the best model of DNNs. Furthermore, this paper is also compared DNNs with other classifiers, such as support vector machine (SVM) and decision tree. The results show DNNs outperform SVM and decision tree methods in all performance metrics. The DNNs performances with three hidden layers as the best model, achieve accuracy, sensitivity, specificity, precision, and F1-score are 98.85%, 95.95%, 99.26%, 94.80%, and 95.36%, respectively. In the future, DNNs are more performing with the automated feature representation in AND processing.
Author identification in bibliographic data using deep neural networks Firdaus Firdaus; Siti Nurmaini; Reza Firsandaya Malik; Annisa Darmawahyuni; Muhammad Naufal Rachmatullah; Andre Herviant Juliano; Tio Artha Nugraha; Varindo Ockta Keneddi Putra
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 3: June 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i3.18877

Abstract

Author name disambiguation (AND) is a challenging task for scholars who mine bibliographic information for scientific knowledge. A constructive approach for resolving name ambiguity is to use computer algorithms to identify author names. Some algorithm-based disambiguation methods have been developed by computer and data scientists. Among them, supervised machine learning has been stated to produce decent to very accurate disambiguation results. This paper presents a combination of principal component analysis (PCA) as a feature reduction and deep neural networks (DNNs), as a supervised algorithm for classifying AND problems. The raw data is grouped into four classes, i.e., synonyms, homonyms, homonyms-synonyms, and non-homonyms-synonyms classification. We have taken into account several hyperparameters tuning, such as learning rate, batch size, number of the neuron and hidden units, and analyzed their impact on the accuracy of results. To the best of our knowledge, there are no previous studies with such a scheme. The proposed DNNs are validated with other ML techniques such as Naïve Bayes, random forest (RF), and support vector machine (SVM) to produce a good classifier. By exploring the result in all data, our proposed DNNs classifier has an outperformed other ML technique, with accuracy, precision, recall, and F1-score, which is 99.98%, 97.98%, 97.86%, and 99.99%, respectively. In the future, this approach can be easily extended to any dataset and any bibliographic records provider.
Text Classification Using Long Short-Term Memory With GloVe Features Winda Kurnia Sari; Dian Palupi Rini; Reza Firsandaya Malik
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 5, No 2 (2019): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (771.053 KB) | DOI: 10.26555/jiteki.v5i2.15021

Abstract

In the classification of traditional algorithms, problems of high features dimension and data sparseness often occur when classifying text. Classifying text with traditional machine learning algorithms has high efficiency and stability characteristics. However, it has certain limitations with regard to large-scale dataset training. Deep Learning is a proposed method for solving problems in text classification techniques. By tuning the parameters and comparing the eight proposed Long Short-Term Memory (LSTM) models with a large-scale dataset, to show that LSTM with features GloVe can achieve good performance in text classification. The results show that text classification using LSTM with GloVe obtain the highest accuracy is in the sixth model with 95.17, the average precision, recall, and F1-score are 95
Hubungan Prilaku Remaja Berinternet di Inderalaya Reza Firsandaya Malik; Deris Stiawan; Erwin Erwin; Rossi Passarella; sutarno sutarno; Sarmayanta Sembiring; Ahmad Heryanto
Annual Research Seminar (ARS) Vol 1, No 1 (2015)
Publisher : Annual Research Seminar (ARS)

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Abstract

Indralaya known as the City of Students and Education, also has more than 30 boarding school, and 60 senior high school / vocational school spread over Ogan Ilir. Implementation of community service performed in Inderalaya to determine the relationship of the behavior of teenagers surfs the internet and provide knowledge about healthy and safe while surfing an internet. The method of data collection using questionnaires. Number of questionnaires distributed as many as 27 participants from SMAN 1 Inderalaya Utara and Pondok Pesantren Al Ittifaqiyah based on age from 15 to 17 years. Results showed teenagers have the tendency to access the internet every day with a duration of 1-2 hours and mostly done in the bedroom. Applications often they use is social networking. The teens also have the courage to meet with people who are known via online with a percentage of 29.6%.
Penerapan Metode K-Nearest Neighbor dalam Memprediksi Masa Studi Mahasiswa (Studi Kasus : Mahasiswa STIKOM Dinamika Bangsa) Jasmir Jasmir; Dodo Zaenal Abidin; Siti Nurmaini; Reza Firsandaya Malik
Annual Research Seminar (ARS) Vol 3, No 1 (2017): ARS 2017
Publisher : Annual Research Seminar (ARS)

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Abstract

Ledakan pertumbuhan data mahasiswa yang terjadi dalam lembaga perguruan tinggi akan menjadi tumpukan data yang sangat luar biasa. Dengan adanya tumpukan data ini penulis mencoba memanfaatkannya untuk mencari informasi baru. Di STIKOM Dinamika Bangsa tumpukan data yang digunakan merupakan data akademik yaitu data IP (Indeks Prestasi) mahasiswa dari semester satu sampai semester enam. Dengan adanya data ini, penulis melakukan prediksi tentang masa studi mahasiswa. Data dari semester satu sampai semester enam ini akan menjadi dasar perhitungan prediksi yang penulis lakukan dengan tujuan dapat menemukan informasi mahasiswa yang bisa lulus tepat waktu dan mahasiswa yang lulus tidak tepat waktu dengan menggunakan salah satu teknik data mining yaitu metode klasifikasi k-Nearest Neighbor dengan  bantuan Microsoft excel.  Hasil dari penelitian ini adalah nilai prediksi kelulusan mahasiswa dengan berbagai nilai k nya.
Pemanfaatan Jaringan Nirkabel Untuk Komunikasi Data dan Suara di SMK Teknik Komputer dan Jaringan di Palembang Reza Firsandaya Malik; Deris Stiawan; Erwin Erwin; Rossi Passarella; Sutarno Sutarno; Ahmad Fali Oklilas; Ahmad Heryanto
Annual Research Seminar (ARS) Vol 1, No 1 (2015)
Publisher : Annual Research Seminar (ARS)

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Abstract

According to the Basic Data of Directorate of Technical and Vocational Education that there are 18 vocational Computer Engineering and Networks in Palembang with consist of 2 state and 16 private schools. Utilization of the wireless network delivered in the training method that involves cognitive, affective and psychomotor aspects. The basic training taught basic computer network, especially related to wireless networks. These three aspects are evaluated through a pre test and post test. The results of pre and post test showed that 6.7% of the participants were not able to increase their knowledge about the wireless network while 93.3% are able.
Evaluasi Carrier Sense Multiple Access/Collision Avoidance Berbasis Opportunistic Random Access Reza Firsandaya Malik; Erick Okvanty Haris
Annual Research Seminar (ARS) Vol 4, No 1 (2018): ARS 2018
Publisher : Annual Research Seminar (ARS)

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Abstract

Makalah ini memaparkan mengenai pengiriman data dengan proses CSMA/CA berbasis Opportunistic Random Access. Adapun yang menjadi latar belakang penulisan ini karena sering adanya proses pengiriman data yang hilang sebelum sampai atau packet lost. Evaluasi yang dilakukan menggunakan tools Omnet++. Skenario yang dilakukan dengan menggunakan 2 jenis node dan 2 jenis variasi kecepatan. Parameter – parameter Quality of Service seperti Delay, Throughput, Jitter, dan Routing Overhead digunakan dalam evaluasi kinerja algoritma Opportunistic Random Access dalam proses CSMA/CA. Hasil simulasi didapatkan bahwa proses  CSMA/CA menggunakan Opportunistic Random Acces memberikan efisiensi terhadap waktu dan efisiensi paket terhadap proses yang hanya menggunakan CSMA/CA saja.
Co-Authors achmad Nopransyah Aditya P.P. Prasetyo Ahmad Fali Oklilas Ahmad Heryanto Andre Herviant Juliano Bambang Tutuko Darmawahyuni, Annisa Dede Triseptiawan Deris Stiawan Dian Palupi Rini Dodo Zaenal Abidin Erick Okvanty Haris Erwin, Erwin Fariz Abdul Aziz Firdaus Firdaus Hashim Hashim Hermawan Hermawan Jasmir Jasmir Jasmir Jasmir Muhammad Aris Ganiardi Muhammad Hafiz Muhammad Naufal Rachmatullah Muhammad Reyhan Zalbina RA Fattah Adriansyah Razali Ngah Rikza Azriyan Rossi Passarella Saparudin Saparudin Sarmayanta Sembiring Septian, Tri Wanda Siti Nurmaini Siti Zaiton Mohd Sutarno Sutarno Tharek Abdul Rahman Tifanny Nabarian Tio Artha Nugraha Varindo Ockta Keneddi Putra Winda Kurnia Sari