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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) SITEKIN: Jurnal Sains, Teknologi dan Industri JUSIFO : Jurnal Sistem Informasi Jurnal Ilmiah Rekayasa dan Manajemen Sistem Informasi Sistemasi: Jurnal Sistem Informasi JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JURNAL MEDIA INFORMATIKA BUDIDARMA Seminar Nasional Teknologi Informasi Komunikasi dan Industri Jurnal Inotera JURIKOM (Jurnal Riset Komputer) Jurnal Sistem Cerdas Building of Informatics, Technology and Science Jurnal Sistem Informasi dan Informatika (SIMIKA) Indonesian Journal of Electrical Engineering and Computer Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Indonesian Journal of Data and Science Jurnal Teknik Informatika (JUTIF) KLIK: Kajian Ilmiah Informatika dan Komputer Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Indonesian Journal of Informatic Research and Software Engineering Malcom: Indonesian Journal of Machine Learning and Computer Science CONSEN: Indonesian Journal of Community Services and Engagement Journal of Informatics, Electrical and Electronics Engineering The Indonesian Journal of Computer Science INOVTEK Polbeng - Seri Informatika Jurnal Ilmu Komputer dan Teknik Informatika Jurnal Pengabdian Masyarakat Berdampak
Inggih Permana
Universitas Islam Negeri Sultan Syarif Kasim Riau
Religion Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Other

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Analisis Sentimen Terhadap Pemindahan Ibu Kota Negara Menggunakan Algoritma Naive Bayes Classifier dan K-Nearest Neightbors Dedi Pramana; M Afdal; Mustakim Mustakim; Inggih Permana
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 3 (2023): Juli 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i3.6523

Abstract

The relocation of Indonesia's capital city is a hot topic of discussion at the moment. So that this government policy reaps a lot of reactions from various parties, especially the general public in Indonesia. Various reactions were shown with various expressions on various social media. One of the social media that has become a place for people to express themselves in responding to this government policy is Instagram. The comments poured by the community on posts on Instagram are very diverse ranging from positive, negative, and neutral comments. If these comments are processed properly, they can be used as evaluation material for the relocation of the State capital. Seeing this, a sentiment analysis is needed which is intended to classify the various comments so that they can be presented into information which will be intended to help the government make considerations in carrying out policies towards moving the national capital. In this study, data processing was carried out with the Naive Bayes Classifier and K-Nearest Neightbors algorithms with Instagram comment data on posts related to moving the national capital. Where the amount of data used is 2,404 comments. It was found that the accuracy of the NBC algorithm was 63.09% and K-Nearest Neightbors was 69.23% so it can be concluded that KNN is better than NBC. In addition, the popularity of public sentiment towards the relocation of the National Capital was also obtained with a positive sentiment of 28% totaling 643 comments, a neutral sentiment of 42% totaling 1025 comments, and a negative sentiment of 30% totaling 730 comments.
Penerapan Algoritma Association Rules Dalam Penentuan Pola Pembelian Berdasarkan Hasil Clustering Sania Fitri Octavia; Mustakim Mustakim; Inggih Permana; Siti Monalisa
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 3 (2023): Juli 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i3.6129

Abstract

Zanafa Bookstore is one of the bookstores in Pekanbaru city that is required to meet customer needs and has the right focus in developing sales strategies every day. During the new school year there is an increase in sales, it is known that in July there are the most purchase transactions which are the beginning of the new school year for students and students. In addition, the placement of the book layout is only based on the employee's estimated shelf so that it will affect the convenience of consumers in choosing and finding books if the books are arranged far apart. By placing the layout in accordance with consumer purchasing patterns, it can improve the quality of customer service in bookstores. The book layout can also be used as a reference when adding book stock, information is needed by utilizing transaction data using data mining, namely by using Association rules commonly called Market Basket Analysis. This research uses K-Medoid for clustering on Apriori and FP-Growth in generating rule patterns on large-scale data. Several experiments were conducted on K-Medoid starting from cluster 2 to cluster 7, each of which will be applied to Apriori and FP-Growth with 30% support and 70% confidence. By comparing the evaluation results of each algorithm with each other, it is known that FP-Growth has superior results to Apriori with a total strength of rules of 1.2012. So that the results of the association rules obtained can be used as a reference in the placement of book layouts in the Zanafa bookstore.
A Comparative Study of Student Satisfaction Levels on Online Learning Using K-NN and Naïve Bayes Hilda Mutiara Nasution; Mustakim Mustakim; Inggih Permana; M. Afdal
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 7 No. 1 (2023): Issues July 2023
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v7i1.9581

Abstract

The outbreak of the Covid-19 pandemic in Indonesia led to restrictions on human social activities to minimize transmission. Teaching-learning is also affected when students must stay home and follow distance learning based on Government Regulation Number 21 of 2020, the Large-Scale Social Restrictions (PSBB) policy, issued on March 31, 2020. This has led to the emergence of learning support applications such as Zoom, Google Classroom, Google Meet, E-Learning, and many more. However, this new learning culture requires adaptation for effective implementation. During the adaptation process, researchers want to measure the level of student satisfaction and find out the best algorithm for classifying the level of student satisfaction. This measurement uses two data mining algorithms, K-Nearest Neighbour (K-NN) and Naïve Bayes, and the Islamic State University of Sultan Syarif Kasim Riau students as the research object. Different algorithms have varying strengths and weaknesses in handling specific data types and classification tasks. By comparing both algorithms, we can assess their generalization capabilities. A model that performs well on training data but fails to generalize to unseen data may not be as effective as a more robust algorithm that exhibits better generalization performance. K-NN classification with a value of k = 3 gets good results. Based on the study results, the conclusion is that K-NN is more optimal in classifying student satisfaction levels than Naïve Bayes with an accuracy ratio of 85% : 80%, precision of 85% : 84%, and recall of 99% : 93%.
Model for Estimating Waste Generation in Pekanbaru Using Backpropagation Algorithm Farahdina Risky Ramadani; Inggih Permana; M. Afdal; Siti Monalisa
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 7 No. 1 (2023): Issues July 2023
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v7i1.9767

Abstract

Waste generation in Pekanbaru City cannot be managed optimally. Based on 2020 data, less than 50% of the waste that reaches the Final Disposal Site (TPA) reaches. To overcome this problem, this study aims to create an estimation model that can estimate the amount of waste generated each year. So that it can help the authorities to implement various policies to control waste generation. The estimation model is created using the backpropagation algorithm. The attributes used are those related to population and waste generation. Based on the results of experiments conducted using RapidMiner, the best network architecture model is the 6-6-1 model, namely six nodes in the input layer, six nodes in the hidden layer, and one node in the output layer. The six nodes in the input layer refer to the number of attributes used. The activation function used is binary sigmoid. The RMSE value generated from the best model is very low, namely 0.0181. So it can be concluded that this model can be used to estimate the generation of solid waste in Pekanbaru City
Penerapan Algoritma Fuzzy C-Means Pada Segmentasi Pelanggan B2B dengan Model LRFM Aufa Zahrani Putri; M Afdal; Siti Monalisa; Inggih Permana
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 3 (2023): Juli 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i3.6150

Abstract

PT. XYZ is one of the major pharmaceutical industries in Indonesia by marketing its products through B2B (Business to Business) customers. PT. XYZ doesn't understand what customers need. PT. XYZ also implements a cashback system for B2B customers. This study aims to determine customer segmentation, analysis of customer characteristics, firmgration and proposed strategies provided by researchers to PT. XYZ. Loyalty and customer characteristics are very influential on a company. To show which customers are loyal to the company, the Fuzzy C-Means algorithm is used to cluster and the Davies Bouldien Indeks (DBI) is used for the clustering algorithm results. The algorithm used is according to the Length, Recency, Frequency and Monetary (LRFM) model to classify purchasing behavior. It can be seen from the frequency variable which customers are loyal to which companies are not. Then determine the firmography using the attributes of business entity type, customer type, and location. After determining loyal and non-loyal customers, the analysis of customer characteristics is divided into 4 parts, namely the Superstar Segment or the best customer, which is located in cluster 2 where customers in cluster 2 can have a long-term relationship with the company, then the Golden Segment or which has the second highest value (monetary) is located in cluster 4, then the Average Value Segment or the customer who has the average value of all segments is located in cluster 5 and the Dormant Segment or the lowest customer is located in cluster 3 where customer 3 has little relationship with the company.
Determining the Final Project Topic Based on the Courses Taken by Using Machine Learning Techniques Vicky Salsadilla; Inggih Permana; Muhammad Jazman; M. Afdal
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 3 No. 2 (2023): MALCOM October 2023
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v3i2.904

Abstract

A thesis (TA) is a scientific paper based on a problem. TA must be completed by students who wish to complete their studies. During this time, students often experience difficulties in determining the TA topic they want to research. To fix it, this research tries to determine TA topics using Machine Learning (ML) techniques based on the elective courses that students have taken. Elective courses are one form of academic data that can be used to consider TA topics. The ML algorithms used are KNN, NBC, ANN, SVM, C4.5, Random Forest, and Logistic Regression. The dataset used in this research is imbalanced data. This research balances the data using the Random Oversampling method and the Random Undersampling method. The results of experiments show that datasets balanced using ROS produce much higher ML performance, but tend to over-fit due to data duplication in the dataset. If the dataset is not balanced at all then the ML performance will be very low. Therefore, for unbalanced data, it is recommended to use the RUS method as data balance. The highest accuracy results for algorithms balanced using ROS are ANN=69.7%, RF=66.7%, SVM=57.6%, LR=57.6%, NBC=42.4%, C4.5=42.4%, and KNN=33.3%
Implementasi AHP Untuk Sistem Pendukung Keputusan Penentuan Prioritas Pembangunan Infrastruktur Daerah Wido Purnama; M. Afdal; Inggih Permana; Siti Monalisa
Journal of Computer System and Informatics (JoSYC) Vol 4 No 3 (2023): May 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v4i3.3446

Abstract

BAPPEDA Indragiri Hulu is a governmental organization in Indragari Hulu Regency that aids the government in accomplishing development planning, research, and development-related tasks. However, determining development priorities presents obstacles stemming from the lack of calculation methods and visualization maps to more easily identify the locations of construction sites. To address this issue, a decision support system utilizing the Analytical Hierarchy Process method was constructed. This system considers various criteria, including condition, budget, urgency, usability, and durability, to compare and identify the best development option. Additionally, the system features a Web GIS map visualization function to aid in identifying construction sites. The study found that improving the road in Pematang Rebah City, Rengat Barat District, was the most important alternative, followed by creating box covers for the Tanah Datar Sibabat road in Seberida District and improving roads in Rengat City in Rengat District. This web-based system ranks development priorities and can effectively assist in determining regional development priorities. Based on the results of the Blackbox test, this system is deemed 100% valid, while the User Acceptance Test on this system yields an 81% score, which is highly satisfactory.
Pendekatan Machine Learning: Analisis Sentimen Masyarakat Terhadap Kendaraan Listrik Pada Sosial Media X Kusuma, Gathot Hanyokro; Permana, Inggih; Salisah, Febi Nur; Afdal, M.; Jazman, Muhammad; Marsal, Arif
JUSIFO : Jurnal Sistem Informasi Vol 9 No 2 (2023): JUSIFO (Jurnal Sistem Informasi) | December 2023
Publisher : Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Islam Negeri Raden Fatah Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19109/jusifo.v9i2.21354

Abstract

Environmental issues and the depletion of fossil fuels continue to escalate as the number of fossil fuel-based vehicle users increases in Indonesia. Electric vehicles emerge as one of the potential alternative solutions to address current environmental challenges, given their eco-friendly nature and lack of pollution emissions. Sentiment analysis is conducted to understand public responses, both supportive and opposing, towards electric vehicles. This research aims to analyze the sentiment of X-social media users regarding electric vehicles using machine learning techniques. The research stages include data collection, data selection, preprocessing, and classification using Naïve Bayes Classifier (NBC), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) algorithms. The test results show that on a balanced dataset using ROS, SVM performs the best with accuracy = 68.7%, precision = 77.9%, and recall = 68.4%. Meanwhile, NBC yields an accuracy of 60.3%, precision of 61.3%, and recall of 60.3%, while KNN has an accuracy of 53.9%, precision of 54%, and recall of 53.9%.
Perbandingan Algoritma KNN, NBC, dan SVM: Analisis Sentimen Masyarakat Terhadap Perparkiran di Kota Pekanbaru Intan, Sofia Fulvi; Permana, Inggih; Salisah, Febi Nur; Afdal, M.; Muttakin, Fitriani
JUSIFO : Jurnal Sistem Informasi Vol 9 No 2 (2023): JUSIFO (Jurnal Sistem Informasi) | December 2023
Publisher : Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Islam Negeri Raden Fatah Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19109/jusifo.v9i2.21357

Abstract

The public response in Pekanbaru to parking policies and regulations has given rise to various sentiments, both positive and negative. This discussion extends not only within the local community but also across various social media platforms. This research aims to analyze public sentiment towards the new parking policies and regulations in the Pekanbaru area. The study involves the KNN, NBC, and SVM algorithms to classify public sentiment into positive, neutral, and negative categories. Balancing techniques used in this research include Random Over Sampling (ROS) and Random Under Sampling (RUS). The data utilized in this study were obtained from posts on the social media platform X. The testing of the dataset using ROS resulted in high accuracy, precision, and recall values. The findings of this research indicate that overall, the SVM algorithm outperforms KNN and NBC in terms of accuracy, precision, and recall. Additionally, the most dominant sentiment is negative, with 422 tweets expressing dissatisfaction with the current parking policies.
A Comparative Study of the Performance of KNN, NBC, C4.5, and Random Forest Algorithms in Classifying Beneficiaries of the Kartu Indonesia Sehat Program Nabillah, Putri; Permana, Inggih; Afdal, M.; Muttakin, Fitriani; Marsal, Arif
JUSIFO : Jurnal Sistem Informasi Vol 10 No 1 (2024): JUSIFO (Jurnal Sistem Informasi) | June 2024
Publisher : Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Islam Negeri Raden Fatah Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19109/jusifo.v10i1.21536

Abstract

This study evaluates the performance of various algorithms in determining eligible recipients for the Kartu Indonesia Sehat program. The Random Forest algorithm demonstrated the highest accuracy, precision, and recall, with values of 72.08%, 72.41%, and 99.64%, respectively. The emphasis on recall helps minimize errors in identifying eligible recipients. Additionally, the C4.5 algorithm reduced the total number of variables from 33 to 8, highlighting its computational efficiency. The findings provide valuable insights for the Social Affairs Office of Dumai City in making informed decisions regarding KIS eligibility. The results underscore the effectiveness of using algorithmic approaches to enhance the accuracy and efficiency of aid distribution processes.
Co-Authors Aditya Nugraha Yesa Agus Buono Ahsyar, Tengku Khairil Al Kiramy, Razanul Alfakhri, Rezky Alfaridzi, Gemma Tahmid Aliya, Rahma Andi Darlianto Andriyani, Dwi Ratna Anggi Widya Atma Nugraha Anggia Anfina Anisah Fitri Anjani, Yulia Merry Annisa Ramadhani Aprijon Arif Marsal Arif Marsal Arif Marsal Arifah Fadhila Andaranti Arifin, Abdullah Aufa Zahrani Putri Aulia Dina Bib Paruhum Silalahi Chinthia, Maulidania Mediawati Dedi Pramana Dessi Cahyanti Detha Yurisna Detha Yurisna Devi, Rahma Dzul Asfi Warraihan Eka Pandu Cynthia Eki Saputra Eki Saputra Endah Purnamasari Esis Srikanti Fadhilah Syafria Fadil Rahmat Andini Farahdina Risky Ramadani Febi Nur Salisah Febi Nur Salisah Fiki Fikri, M. Hayatul Fitriah, Ma’idatul Fitriah, Ma’idatul Fitriani Muttakin Fitriani Muttakin Fitriani Muttakin Gathot Hanyokro Kusuma Gurning, Umairah Rizkya Hafiz Aryan Siregar Hasbi Sidiq Arfajsyah Hendri, Desvita Hilda Mutiara Nasution Husaini, Fahri Idria Maita Idria Idriani R, Nova Ikhsani, Yulia Imam Muttaqin Intan, Sofia Fulvi Ismail Marzuki Jazma, Muhammad Jazman , Muhammad Jazman, Muhammad Kusuma, Gathot Hanyokro M Afdal M Afdal M Zaky Ramadhan Z M. Afdal M. Afdal M. Afdal M. Afdal M. Afdal Maulana, Rizki Azli Megawati Megawati - Mona Fronita, Mona Muhammad Afdal Muhammad Fikry Muhammad Jazman Muhammad Jazman Muhammad Jazman Muhammad Naufal, Muhammad Muhammad Zacky Raditya Mukmin Siregar Mundzir, Mediantiwi Rahmawita Munzir, Medyantiwi Rahmawita Mustakim Mustakim Mustakim Mustakim Mustakim Mustakim Mutia, Risma Muttakin, Fitriani Nabillah, Putri Nardialis Nardialis Nasution, Nur Shabrina Naufal Fikri, R. Adlian Negara, Benny Sukma Nesdi Evrilyan Rozanda Nesdi Evrilyan Rozanda Nisa', Sayyidatun Norhavina Norhavina Nunik Noviana Kurniawati Nurainun Nurainun Nuraisyah Nuraisyah Nurfadilla, Nadia Nurkholis Nurkholis nursalisah, febi Octavia, Sania Fitri Pratama, Arya Yendri Priady, Muhamad Ilham Pristiawati, Andani Putri Puput Iswandi Putra, Moh Azlan Shah Putra, Tandra Adiyatma Rahman, Eman Rahmawita M, Medyantiwi Rangga Arief Putra Rayean, Rival Valentino Restu Ramadhan Ria Agustina Rice Novita Rice Novita Rizka Fitri Yansi Rizki Pratama Putra Agri Rozanda, Nesdi Evrilyan Sabillah, Dian Ayu Salisah, Pebi Nur Sania Fitri Octavia Sanusi Shir Li Wang Siti Monalisa Sofia Fulvi Intan Susanti, Pingki Muliya Tasya Marzuqah Tengku Khairil Ahsyar Triningsih, Elsa Tshamaroh, Muthia Uci Indah Sari Ula, Walid Alma Vicky Salsadilla Wenda, Alex Wido Purnama Winda Wahyuti Windy Amelia Putri Wira Mulia, M. Roid Yusmar Yusmar Zarnelly Zarnelly Zarnelly Zarnelly Zarnelly Zarnelly Zarnelly