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All Journal Jurnal Sains dan Teknologi Jurnas Nasional Teknologi dan Sistem Informasi ANDHARUPA CogITo Smart Journal Indonesian Journal of Artificial Intelligence and Data Mining JITK (Jurnal Ilmu Pengetahuan dan Komputer) JMM (Jurnal Masyarakat Mandiri) SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan ILKOM Jurnal Ilmiah DoubleClick : Journal of Computer and Information Technology Matrik : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Pengabdian Kepada Masyarakat MEMBANGUN NEGERI Seminar Nasional Teknologi Informasi Komunikasi dan Administrasi [SEMINASTIKA] Scientific Journal of Informatics Indonesian Journal of Data and Science Society : Jurnal Pengabdian dan Pemberdayaan Masyarakat TIERS Information Technology Journal Jurnal Ilmiah IT CIDA : Diseminasi Teknologi Informasi Jurnal Ilmiah Teknik Mesin, Elektro dan Komputer (JURITEK) Jurnal Pengabdian Mitra Masyarakat (JPMM)
Pungkas Subarkah
Universitas Amikom Purwokerto
Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Journal : ILKOM Jurnal Ilmiah

 1 
Sentiment analysis of customer satisfaction levels on smartphone products using Ensemble Learning Muhammad Ma’ruf; Adam Prayogo Kuncoro; Pungkas Subarkah; Faridatun Nida
ILKOM Jurnal Ilmiah Vol 14, No 3 (2022)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v14i3.1377.339-347

Abstract

Increasingly sophisticated technological developments create new ways for people to conduct trading business. An example of this technology application is the use of e-commerce. However, there are conditions where the seller cannot measure the level of satisfaction and identify problems experienced by his customers if it is only based on the rating as the case in smartphones transactions. Therefore, a solution is needed to create a system that can filter negative and positive comments. This study offers a solution to address this issue by using machine learning employing the K-Nearest Neighbors, SVM, and Naive Bayes algorithms with hyperparameters from previous studies. This study applied the ensemble learning method with the Voting Classifier technique, which is an algorithm to combine several algorithms that have been made. From the test results, the highest accuracy was obtained by SVM with an accuracy value of 91.18% while the ensemble learning method obtained an accuracy value of 89.22%. The difference in the accuracy of training and testing for SVM and ensemble learning method is 7.1% and 4% respectively. These results indicate that the ensemble learning method can help improve the performance of sentiment analysis algorithms for comments on smartphone products.
Comparison of correlated algorithm accuracy Naive Bayes Classifier and Naive Bayes Classifier for heart failure classification Pungkas Subarkah; Wenti Risma Damayanti; Reza Aditya Permana
ILKOM Jurnal Ilmiah Vol 14, No 2 (2022)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v14i2.1148.120-125

Abstract

Heart failure (ARF) is a health problem that has relatively high mortality and morbidity rates in developed or developing countries, including Indonesia. In 2016, WHO stated that 17.5 million people died from cardiovascular disease, while in 2008, HF disease represented 31% of patient deaths worldwide. One of the new breakthroughs for early diagnosis is utilizing data mining techniques. In this study, the Correlated Naive Bayes Classifier (C-NBC) and Naive Bayes Classifier (NBC) algorithms are used to obtaining the best accuracy results so that they can be used for the Heart Failure dataset. Based on the results of the tests that have been carried out, it shows that the Correlated Naive Bayes Classifier (C-NBC) algorithm accuracy of 80.6% obtains higher accuracy than the Naive Bayes Classifier (NBC) algorithm of 67.5%. With the results of this study, the use of the Correlated Naive Bayes Classifier (C-NBC) algorithm can be used to diagnose patients with heart failure (heart failure) because it has a high level of accuracy and is categorized as Good Classification.
Rectified Linear Units and Adaptive Moment Estimation Optimizer on ANN with Saved Model Prediction to Improve The Stock Price Prediction Framework Performance Sekhudin Sekhudin; Yuli Purwati; Fandy Setyo Utomo; Mohd Sanusi Azmi; Pungkas Subarkah
ILKOM Jurnal Ilmiah Vol 15, No 2 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i2.1586.271-282

Abstract

A stock is a high-risk, high-return investment product. Prediction is one way to minimize risk by estimating future prices based on past data. There are limitations to solving the stock prediction problem from previous research: limited stock data, practical aspects of application, and less than optimal stock price prediction results. The main objective of this study is to improve the prediction performance by formulating and developing the stock price prediction framework. Furthermore, the research provides a stock price prediction framework that can produce better prediction results than the previous study with fast computation time. The proposed framework deals with data generation, pre-processing and model prediction. In further, the proposed framework includes two prediction methods for predicting stock closing prices: stored model prediction and current model prediction. This study uses an artificial neural network with Rectified Linear Units as an activation function and Adam Optimizer to predict stock prices. The model we have built for each forecasting method shows a better MAPE value than the model in previous studies. Previous research showed that the lowest MAPE was 1.38% for TLKM shares and 0.81% for BBRI. Our proposed framework based on the stored model prediction method shows a MAPE value of 0.67% for TLKM shares and 0.42% for BBRI. While the current model prediction method shows a MAPE value of 0.69% for TLKM shares and 0.89% for BBRI. Furthermore, the stored model prediction method takes 1.0 seconds to process a single prediction request, while the current model prediction takes 220 seconds.
Co-Authors A. Kholil Hidayat Adam Prayogo Kuncoro Akhmad Mustolih Ali Nur Ikhsan Alya Khansa Dzakkiyah Arbangi Puput Sabaniyah Azizan Nurhakim Bagus Adhi Kusuma Cindy Magnolia Dava Patria Utama Dhanar Saputra Dias Ayu Budi Utami Didit Suhartono Eko Budi Prasetya Enggar Pri Pambudi Erika Luthfi Afifah Fandy Setyo Utomo Faridatun Nida Fiby Nur Afiana Harun Alrasyid Hendra Marcos Herta Tri Wahyu Hidayah, Debby Ummul Irma Darmayanti Isnaini, Khairunnisak Nur Jali Suhaman Laily Farkhah Adhimah Latifah Adi Triana Luki Rafi Anuggilarso Luky Rafi Anunggilarso M. Syaiful Amin Maharani Kusuma Dewi Melida Ratna Utami Mohd Sanusi Azmi Muhammad Marshal Abdallah Muhammad Ma’ruf Nanda Nurisya Merliani Nandang Hermanto Neta Tri Widiawati Nevita Cahaya Ramadani Nikmah Trinarsih Nur Isnaeni Khoerida Nurul Hidayati Prastyadi Wibawa Rahayu Primandani Arsi Puteri Johar Aunillah Ragil Wilujeng Rayinda Maya Anjani Reza Aditya Permana Reza Arief Firmanda Riyanto Riyanto Rizki Sadewo Rofiqul Umma Sarmini - Sarmini Sekhudin Sekhudin Septi Oktaviani Nur Hidayah Septi Oktaviani Nur Hidayah Siti Alvi Solikhatin Siti Muflikhatun Trian Damai Wanda Fitrianingsih Wenti Risma Damayanti Yuli Purwati