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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics JSI: Jurnal Sistem Informasi (E-Journal) Jurnal Informatika Jurnal Informatika Proceeding International Conference on Information Technology and Business JUITA : Jurnal Informatika International conference on Information Technology and Business (ICITB) Annual Research Seminar Journal of Information System JPSriwijaya JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Journal of Information Technology and Computer Science JITK (Jurnal Ilmu Pengetahuan dan Komputer) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Sisfokom (Sistem Informasi dan Komputer) INTECOMS: Journal of Information Technology and Computer Science KACANEGARA Jurnal Pengabdian pada Masyarakat Jurnal ULTIMATICS Jurnal Pendidikan Matematika (JUDIKA EDUCATION) Informatik : Jurnal Ilmu Komputer IJID (International Journal on Informatics for Development) KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) SEINASI-KESI Jurnal Riset Informatika CSRID (Computer Science Research and Its Development Journal) Jurnal Informatika Global CCIT (Creative Communication and Innovative Technology) Journal JSAI (Journal Scientific and Applied Informatics) Journal of Information Systems and Informatics Mulia International Journal in Science and Technical Zonasi: Jurnal Sistem Informasi Indonesian Journal of Electrical Engineering and Computer Science Jurnal Generic Jurnal AbdiMas Nusa Mandiri Jurnal Pendidikan dan Teknologi Indonesia ABDI MOESTOPO: Jurnal Pengabdian pada Masyarakat Proceeding of International Conference Health, Science And Technology (ICOHETECH) Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS Jurnal Abdimas Maduma JUKEMAS : Jurnal Pengabdian Kepada Masyarakat Jurnal Sistem Informasi dan Aplikasi
Ermatita -
Universitas Sriwijaya
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Journal : Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)

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Credit Scoring Kelayakan Debitur Menggunakan Metode Hybrid ANN Backpropagation dan TOPSIS Susan Dwi Saputri; Ermatita Ermatita
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 3 No 1 (2019): April 2019
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (627.952 KB) | DOI: 10.29207/resti.v3i1.847

Abstract

Credit is one of the common practices that provide benefits for financial or non-financial institutions. However on the other hand, aid loans also have higher risks if the institutions give the wrong decision in giving a loan. Credit Scoring is one of techniques that can determine whether it is feasible to given a loan or not. The selection of a credit scoring model greatly determines the value in classifying credit that is feasible or not to giving a loan. Decision Support System (DSS) is one system that can be used to overcome this problem. The advantages of DSS are being able to overcome the problems that have semi-structured and unstructured data. In this study, DSS was supported by using Artificial Neural Network Backpropagation method and TOPSIS method to find the priority for seeking eligibility. Accuracy results obtained in this study reached 98,69% with the number of iteration is 300, the number of training data is 30, neuron hidden 12 and error tolerance is 0.001. TOPSIS method succeeded in ranking 185 data selected as recipients of credit. Keywords:Credit Scoring, Decision Support System (DSS), Artificial Neural Network (ANN), Backpropagation, TOPSIS.
Analisis Pola Prediksi Data Time Series menggunakan Support Vector Regression, Multilayer Perceptron, dan Regresi Linear Sederhana Ika Oktavianti; Ermatita Ermatita; Dian Palupi Rini
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 3 No 2 (2019): Agustus 2019
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (640.713 KB) | DOI: 10.29207/resti.v3i2.1013

Abstract

Licensing services is one of the forms of public services that important in supporting increased investment in Indonesia and is currently carried out by the Investment and Licensing Services Department. The problems that occur in general are the length of time to process licenses and one of the contributing factors is the limited number of licensing officers. Licensing data is a time series data which have monthly observation. The Artificial Neural Network (ANN) and Support Vector Machine (SVR) is used as machine learning techniques to predict licensing pattern based on time series data. Of the data used dataset 1 and dataset 2, the sharing of training data and testing data is equal to 70% and 30% with consideration that training data must be more than testing data. The result of the study showed for Dataset 1, the ANN-Multilayer Perceptron have a better performance than Support Vector Regression (SVR) with MSE, MAE and RMSE values is 251.09, 11.45, and 15.84. Then for dataset 2, SVR-Linear has better performance than MLP with values of MSE, MAE and RMSE of 1839.93, 32.80, and 42.89. The dataset used to predict the number of permissions is dataset 2. The study also used the Simple Linear Regression (SLR) method to see the causal relationship between the number of licenses issued and licensing service officers. The result is that the relationship between the number of licenses issued and the number of service officers is less significant because there are other factors that affect the number of licenses.
Penerapan Knowledge Management System Menggunankan Algoritma Levenshtein Orissa Octaria; Ermatita Ermatita; Sukemi Sukemi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 3 No 2 (2019): Agustus 2019
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (929.81 KB) | DOI: 10.29207/resti.v3i2.1045

Abstract

Knowledge management (KM) is an important thing to store or possess existing knowledge. The difficulty of getting knowledge that has actually been known for a long time about special planning for new information is to repair a certain position, in this case the container that contains several private universities in Palembang. The lecturer can only find out how the system discusses in the college, and many other knowledge that must be discussed by the new lecturer. Therefore the Knowledge Management System (KMS) will be built using the Inukshuk Model to become a means for existing knowledge, while the algorithm for searching knowledge stored in KMS is the Levenshtein Algorithm. The selection of the Levenshtein algorithm itself which uses this algorithm measures the relationship between strings (words to words, words to sentences and sentences to sentences) by calculating the edit distance, so that it will produce a high level of acquisition. The result is a KMS that is important for private universities to store and manage knowledge web-based services to make it easier for today's users to use many internet networks.
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)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (663.716 KB) | DOI: 10.29207/resti.v3i3.1159

Abstract

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%.
Co-Authors Abdiansah, Abdiansah Adi Sutrisman Ahmad Fali Oklilas Ahmad Fali Oklilas Ahmad Sanmorino Aidil Putrasyah Al Farissi Albert Albert Aldin, Moehammad Alfarezy, Reza Ali Amran Ali Bardadi Ali Bardardi Ali Ibrahim Ali Ibrahim Allsela Meiriza, Allsela Andini Dwi Lestari Anita Desiani Apriansyah Putra Arnelawati Artika Arista Ayuputri, Niken Bambang Suprihatin Barlian Khasoggi Barlian Khasoggi Belly, Belly Nagustria bin Awang, Mohd Khalid Budi Prayoga, Muhamad Hafiz Cindo, Mona Dafid Dedik Budianta Deris Stiawan Dian Palupi Rini Dian Palupi Rini Dien Novita Dominica, Alviona Terry Dwi Asa Verano Dwi Lestari, Rizky Dwi Meylitasari Br. Tarigan Dwi Rosa Indah Endang Lestari Ruskan Endy Suherman Erwin, Erwin Eva Darnila Eva Darnila Fajriana, Fajriana Fajriana, Fajriana Falih, Noor Fathiyah, Alyssa Fathoni - Fauza Adelma Syafrizal Fuadi, Wahyu Geovani, Dite Gumay, Naretha Kawadha Pasemah Hadipurnawan Satria Hafyz Sytar, M. Hartini Hartini Hijriani, Nurul Huda Ubaya Huda Ubaya Husnawati Husnawati Ika Oktavianti ina aisyah handayani Indra Maulana Irmanda, Helena Nurramdhani Ispramono Hadi, Sigit Iwan Pahendra Iwan Pahendra Anto Saputra Jaidan Jauhari Johannes Petrus Joko Purnomo Ken Dhita Tania Khairun Nisak, Novrinda Kurniawan, Mochamad Aryo Aji Kurniawan, Rizky Fariz Andry Lovinta Happy Atrinawati M Fariz Januarsyah M. Fariz Januarsyah M. Miftakul Amin Matondang, Nurhafifah Mauliza Mauliza, Mauliza Megah Mulya Meizalina, Mutiara Amalia Mgs Afriyan Firdaus Mira Afrina Mohammed Y. Alzahrani Mona Cindo Monterico Adrian Muhammad Adrezo Muhammad Fachrurrozi Muhammad Qurhanul Rizqie Muhammad Sadli Muhammad Sadli, Muhammad Muhammad, Duwen Imantata Mutammimul Ula Mutia Fadhila Putri Noprisson, Handrie NUNI GOFAR Nurul Chamidah Nurul Mufliha Eka Putri Nurul Mufliha Eka Putri Octaria, Orissa Osvari Arsalan Pacu Putra Pahendra, Iwan Parwito Patimah, Endah Primanita, Anggina Purwita Sari Purwita Sari, Purwita Putra, Erwin Dwika Rachma nia Rahman, Puti Ayu Andhini Rahmat Budiarto Rahmat Izwan Heroza Rahmat Izwan Heroza Rendra Gustriansyah Reza Firsandaya Malik Richardo, M Denny Richki Hardi Rifkie Primartha Rizka Dhini Kurnia Rizka Dhini Kurnia Rizki Kurniati Royan Dwi Saputra Rudhy Ho Purabaya Ruth Mariana Bunga Wadu Safithri, Selviana Rizki Salamah, Fitri Samsuryadi Samsuryadi Shinta Puspasari Soraya, Atika Suci Destriatania Suci Destriatania Sukemi Sukemi Susan Dwi Saputri Susan Dwi Saputri Sytar, M. Hafizh Terttiaavini, Terttiaavini Tjahjanto, Tjahjanto Tryadriani, Rasqia Nurzulia Verano, Dwi Asa Verlly Puspita Wahyu Fuadi Wahyu Ningsih Yadi Utama Yadi Utama Yudha Pratomo Yudha Pratomo Yudha Pratomo Yundari, Yundari Zalika, Indah Zulkardi