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Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)

RESTI
Website

Published by Ikatan Ahli Informatika Indonesia

ISSN : 25800760 EISSN : 25800760 DOI : https://doi.org/10.29207/resti.v2i3.606

Core Subject : Science, Engineering,

Computer Science & IT Engineering

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) dimaksudkan sebagai media kajian ilmiah hasil penelitian, pemikiran dan kajian analisis-kritis mengenai penelitian Rekayasa Sistem, Teknik Informatika/Teknologi Informasi, Manajemen Informatika dan Sistem Informasi. Sebagai bagian dari semangat menyebarluaskan ilmu pengetahuan hasil dari penelitian dan pemikiran untuk pengabdian pada Masyarakat luas dan sebagai sumber referensi akademisi di bidang Teknologi dan Informasi. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) menerima artikel ilmiah dengan lingkup penelitian pada: Rekayasa Perangkat Lunak Rekayasa Perangkat Keras Keamanan Informasi Rekayasa Sistem Sistem Pakar Sistem Penunjang Keputusan Data Mining Sistem Kecerdasan Buatan/Artificial Intelligent System Jaringan Komputer Teknik Komputer Pengolahan Citra Algoritma Genetik Sistem Informasi Business Intelligence and Knowledge Management Database System Big Data Internet of Things Enterprise Computing Machine Learning Topik kajian lainnya yang relevan

Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)

Articles 1,046 Documents

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Analisis Risiko dan Kontrol Perlindungan Data Pribadi pada Sistem Informasi Administrasi Kependudukan Iqbal Santosa; Raras Yusvinindya
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 3 No 3 (2019): Desember 2019
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Sistem Informasi Administrasi Kependudukan (SIAK) is an application used in managing personal data of residents in all cities/districts in Indonesia. Personal data becomes the public attention because if it is not managed properly it will have an impact on one's legal protection and non-compliance with regulations, i.e. Permenkominfo Nomor 20 tahun 2016 about Protection of Personal Data in the Electronic System. Risk analysis and control of personal data protection on SIAK applications are needed so that the personal data management can be carried out properly and comply with regulatory requirements. Data collected for this study are primary data, sourced from direct observations on the application, interview about assets related to SIAK along with possible risks, and also internal organizations documents. Data analysis was performed with a risk analysis using the ISO 31000: 2018 risk management process approach, where the identification of relevant risks refers to the Generic Risk Scenarios COBIT 5 For Risk, and the determination of relevant controls refers to the Department of Defense Instruction 8500.2 and NIST 800-53. This research involves the Head of Department and employees of Disdukcapil XYZ City that are related to the strategic and operational aspects of SIAK. The results of this study are the identification of 23 possible risks that are spread over 5 processes of personal data protection that classified into the medium-high risk level, and proposed risk control consisting of 19 preventive controls, 6 detective controls, and 2 corrective control.

Penggunaan Feature Selection di Algoritma Support Vector Machine untuk Sentimen Analisis Komisi Pemilihan Umum Imam Santoso; Windu Gata; Atik Budi Paryanti
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 3 No 3 (2019): Desember 2019
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

At this time sentiment analysis is very widely used by people to see the extent of people's sentiments towards an object. Objects that can be used in sentiment analysis can be various kinds, for example about the product regarding receipt by consumers, agencies or institutions regarding the performance of the agency. Whereas for this study taking sentiment analysis of the State Institution namely the General Election Commission (KPU) about the sentiments of the implementation of the ELECTION simultaneously and also the results of the implementation of the ELECTION which have become the subject of discussion by netizens on social media. So this research takes retweet data and retention comments from Twitter social media users. The algorithm used in this study is Support Vector Machine (SVM), with optimization of the use of Weight by Correlation Feature Selection (FS). The results of cross validation SVM without FS are 66.49% for accuracy and 0.716 for AUC. Whereas SVM with FS is 81.18% for accuracy and 0.943 for AUC. Very significant improvement with the use of Weight by Correlation Feature Selection (FS).

Prediksi Indeks Harga Konsumen Menggunakan Metode Long Short Term Memory (LSTM) Berbasis Cloud Computing Soffa Zahara; Sugianto; M. Bahril Ilmiddafiq
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 3 No 3 (2019): Desember 2019
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Long Short Term Memory (LSTM) is known as optimized Recurrent Neural Network (RNN) architectures that overcome RNN’s lact about maintaining long period of memories. As part of machine learning networks, LSTM also notable as the right choice for time-series prediction. Currently, machine learning is a burning issue in economic world, abundant studies such predicting macroeconomic and microeconomics indicators are emerge. Inflation rate has been used for decision making for central banks also private sector. In Indonesia, CPI (Consumer Price Index) is one of best practice inflation indicators besides Wholesale Price Index and The Gross Domestic Product (GDP). Since CPI data could be used as a direction for next inflation move, we conducted CPI prediction model using LSTM method. The network model input consists of 28 variables of staple price in Surabaya and the output is CPI value, also the entire development of prediction model are done in Amazon Web Service (AWS) Cloud. In the interest of accuracy improvement, we used several optimization algorithm i.e. Stochastic Gradient Descent (sgd), Root Mean Square Propagation (RMSProp), Adaptive Gradient(AdaGrad), Adaptive moment (Adam), Adadelta, Nesterov Adam (Nadam) and Adamax. The results indicate that Nadam has 4,008 RMSE’s value, less than other algorithm which indicate the most accurate optimization algorithm to predict CPI value.

Performansi Navigasi Robot Leader-Follower menggunakan Algoritma Logika Fuzzy Interval Tipe 2 Gita Fadila Fitriana; Rifki Adhitama
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 3 No 3 (2019): Desember 2019
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

A leader-follower robot is used to perform different tasks without continuous human assistance. The movement of robot leader-follower to environment who do not structure, avoid persecution and achieving goals is very difficult. Related to the problem, the robot leader-follower requires navigating robots independently using Interval Fuzzy Logic Type-2 (IFLT) 2 Algorithm. The IFLT 2 algorithm performance is successfully applied to this leader-follower robot, with 8 base rules less than the Fuzzy Logic Type 1 Algorithm. This simulation, the robot successfully moves to avoid obstacles and go hand in hand with the position of the follower robot always following the position of the robot leader.

Analisis Sentimen Analisis Sentimen E-Wallet Pada Google Play Menggunakan Algoritma Naive Bayes Berbasis Particle Swarm Optimization Suwanda Aditya Aaputra; Didi Rosiyadi; Windu Gata; Syepry Maulana Husain
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 3 No 3 (2019): Desember 2019
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Increasingly sophisticated technology brings various conveniences both in transportation, information, education to the convenience of transactions in shopping, such as the development of E-wallet can now be easily done using a smartphone. From a number of e-wallet products, researchers took a case study, which is OVO product, which is currently being discussed by many groups, especially in the capital of Jakarta today. Customers or clients who are not satisfied with the services or products offered by a company will usually write their complaints on social media or reviews on Google play. However, monitoring and organizing opinions from the public is also not easy. For this reason, we need a special method or technique that is able to categorize these reviews automatically, whether positive or negative. The algorithm used in this study is Naive Bayes Classifier (NB), with the optimization of the use of Particle Swarm Optimization Feature Selection (FS). The results of cross validation NB without FS are 82.30% for accuracy and 0.780 for AUC. Whereas for NB with FS is 83.60% for accuracy and 0.801 for AUC. Very significant improvement with the use of Feature Selection (FS) Particle Swarm Optimization.

Integrasi N-gram, Information Gain, Particle Swarm Optimation di Naïve Bayes untuk Optimasi Sentimen Google Classroom Fajar Pramono; Didi Rosiyadi; Windu Gata
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 3 No 3 (2019): Desember 2019
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

The use of Learning Management System (LMS) applications made by Google with name Google Classroom since 2015 in junior and senior high schools in Bekasi City helps the learning process become easier. However, its use can have positive and negative effects on students. Google Class Sentiment by integrating N-grams, Information Gain, Particle Swarm Optimization, and Naïve Bayes Classifiers that have never been done by researchers before. From the experiments carried out, N-gram can increase the accuracy of 6.7% and AUC 4%, while using PSO can increase the Accuracy of 9.9% and AUC of 10.4%.

Prototype Alat Pengendali Lampu dengan Perintah Suara menggunakan Arduino Uno Berbasis Web Nurul Isna Ganggalia; Apri Junaidi; Fahrudin Mukti Wibowo
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 3 No 3 (2019): Desember 2019
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

The use of electric power for lights often less considered, a lot of lights are on continuously even though it's not used. As a result, a lot of electricity is wasted. This motivated researchers to create innovations of creating a light control system. The light controller system is designed to simplify and benefit the user. For this reason, researchers make light controllers on the web use voice commands that can be done anywhere and anytime using the internet. Making a prototype of a light control system with voice commands utilizes speech to text on the Web Speech API that converts sound into text, then it will be processed into a command of light controllers by the Arduino Uno microcontroller. The researcher used the prototype development method, where through 3 stages starting from Listen to Customer, Design and Building, and Test Drive Evaluations. The testing results are Internet speed and noise level affect the success rate on the use of light control using sound. At 9.9 Mbps internet speed has a success rate of 86% with response time 2.01 second, while at internet speed 1.9 Mbps has a success rate of 65% with response time 2.50 second. At the noise level of 34.5 dB room has a success rate of 86% with response time 2.02 second, while the noise level of 62 dB has a success rate of 72% with response time 2.21 second.

Rancang Bangun Sistem Informasi Manajemen Bank Sampah Studi Kasus Pada Bank Sampah Panggung Berseri (BSPB) Veri Julianto; Hendrik Setyo Utomo; Herpendi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 3 No 3 (2019): Desember 2019
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Waste is the result of the dynamics of life that can cause problems if it is not properly managed. Many methods had been used to help overcome waste management. The Waste Bank is one of the solutions to help solve waste management. Bank Sampah Panggung Berseri is one of the communities that actively carries out waste management around the Pangung village. BSPB has problems related to solid waste management. The management in question is the management of waste data that is still conventional, archiving is not optimal, has not managed customer data savings properly. In this research also added a marketplace feature where people can make transactions from balances obtained from waste sold by buying basic necessities. The method in this research is to collect data at BSPB, analyze data and develop applications using the prototype method. The results of this study are all the features or functions of the system run well by testing the functionality used the Black Box testing method. In testing using the usability testing method has shown the level of satisfaction for the parameters of usability, ease of learning, ease of use and satisfaction gives an average value of 4.38 from the range of values 1-5. This shows that the system made can be said to satisfy BSPB users and customers.

Studi Komparatif Metode Ekstraksi Fitur pada Analisis Sentimen Maskapai Penerbangan Menggunakan Support Vector Machine dan Maximum Entropy Mona Cindo; Dian Palupi Rini; Ermatita
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 3 No 3 (2019): Desember 2019
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Almost all companies use social media to improve their product services and provide after-sales services that allow their customers to review the quality of their products. By using Twitter social media to be an important source for tracking sentiment analysis. Sentiment analysis is one of the most popular studies today, using sentiment analysis companies can analyze customer satisfaction to improve their services. This study aims to analyze airline sentiments with five different features such as pragmatic, lexical n-gram, POS, sentiment, and LDA using the Support Vector Machine and Maximum Entropy methods. The best results can be obtained using the Maximum Entropy method using all feature extraction with an accuracy of 92.7% and in the Support Vector Machine method, the accuracy obtained is 89.2%.

Sistem Pakar Identifikasi Modalitas Belajar Siswa Menggunakan Metode Forward Chaining Asep Kurniawan; Sumijan; Jufriadif Na’am
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 3 No 3 (2019): Desember 2019
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Student learning modalities are important to be identified by teachers and students. Because the success of students in the field of academics is supported by the appropriate student learning modalities. Often occurs in the process of teaching and learning teachers do not know the modalities of student learning so that the material in teaching teachers difficult to accept by students. Appropriate learning modalities that are in accordance with the methods taught by the teacher need to be built in an Expert System. Expert System that is processed in this research is taken from the expertise of teachers of Senior High School Counseling Guidance 1 Tilatang Kamang by using Forward Chaining method. Learning modalities are processed using expert systems created with php programming languages and mysql databases. Furthermore, this expert system can determine the modalities of visual learning, auditory and kinesthetic. The result of testing on this method is able to determine the learning modality in the students with the accuracy and the speed is good. Expert system test results have been able to determine student learning modalities clearly and can already be recommended to help teachers and students in improving the way students learn the right.

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Yuhefizar

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Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name Yuhefizar

Contact Email jurnal.resti@gmail.com

Phone +628126777956

Journal Mail Official ephi.lintau@gmail.com

Editorial Address Politeknik Negeri Padang, Kampus Limau Manis, Padang, Indonesia.

Location , INDONESIA

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Contact Name
Yuhefizar

Contact Email
jurnal.resti@gmail.com

Phone
+628126777956

Journal Mail Official
ephi.lintau@gmail.com

Editorial Address
Politeknik Negeri Padang, Kampus Limau Manis, Padang, Indonesia.

Location
,

INDONESIA