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All Journal JUSIFO : Jurnal Sistem Informasi Sistemasi: Jurnal Sistem Informasi JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Sistem Cerdas Building of Informatics, Technology and Science Jurnal Sistem informasi dan informatika (SIMIKA) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Indonesian Journal of Informatic Research and Software Engineering Malcom: Indonesian Journal of Machine Learning and Computer Science The Indonesian Journal of Computer Science
Inggih Permana
Jurusan Sistem Informasi Universitas Islam Negeri Sultan Syarif Kasim Riau
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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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.
Reduksi Jumlah Aturan Penentuan Kata Tunjuk dalam Bahasa Arab Menggunakan Algoritma ID3 Permana, Inggih; Febi Nur Salisah, Febi Nur Salisah
Indonesian Journal of Informatic Research and Software Engineering (IJIRSE) Vol. 1 No. 2 (2021): Indonesian Journal of Informatic Research and Software Engineering
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (839.751 KB) | DOI: 10.57152/ijirse.v1i2.144

Abstract

Bahasa Arab memiliki tidak kurang dari sepuluh bentuk kata tunjuk yang bisa digunakan. Untuk menentukan kata tunjuk tersebut harus memperhatikan empat variabel pada objek yang ditunjuk. Dari keempat variabel tersebut menghasilkan 24 kondisi (aturan) yang mungkin muncul untuk menentukan kata tunjuk. Banyaknya aturan yang mungkin muncul tersebut menjadi salah satu sebab pelajar Bahasa Arab melakukan kesalahan menggunakan kata tunjuk. Berdasarkan permasalahan tersebut, maka penelitian ini telah mereduksi aturan tersebut dengan menggunakan Algoritma ID3. Pohon keputusan hasil Algoritma ID3 berhasil mereduksi aturan sebanyak 50%. Selain itu, berdasarkan pohon keputusan tersebut dapat disimpulkan bahwa untuk menentukan kata tunjuk cukup memperhatikan tiga variabel saja.
Pengaruh Normalisasi Data Terhadap Performa Hasil Klasifikasi Algoritma Backpropagation: The Effect of Data Normalization on the Performance of the Classification Results of the Backpropagation Algorithm Permana, Inggih; Salisah, Febi Nur Salisah
Indonesian Journal of Informatic Research and Software Engineering (IJIRSE) Vol. 2 No. 1 (2022): Indonesian Journal of Informatic Research and Software Engineering
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (682.262 KB) | DOI: 10.57152/ijirse.v2i1.311

Abstract

Keberhasilan Algoritma Backpropagation (BP) tergantung pada kualitas data. Sehingga, normalisasi data merupakan proses yang penting. Akan tetapi, beberapa penelitian juga ada yang tidak menggunakan normalisasi data. Oleh sebab itu, penelitian ini mengukur pengaruh normalisasi data terhadap performa hasil klasifikasi Algoritma Backpropagation. Agar diketahui apakah normalisasi benar-benar bisa meningkatkan performa hasil klasifikasi pada Algoritma BP. Penelitian ini menggunakan tiga metode normalisasi data, yaitu: MinMax Normalization; MaxAbs Normalization; dan Z-Score Normalization. Berdasarkan hasil percobaan didapat bahwa jika data yang digunakan terdapat perbedaan rentang nilai antar atribut yang tidak berbeda jauh, maka BP tanpa normalisasi data bisa menjadi pilihan terbaik. Akan tetapi jika pada data terdapat atribut yang memiliki perbedaan rentang nilai yang jauh dari atribut lainnya, maka menggunakan normalisasi data bisa menjadi pilihan terbaik. Berdasarkan hasil percobaan juga didapat bahwa Z-Score Normalization merupakan metode normalisasi terbaik.
Penerapan Metode Regresi Linier Untuk Prediksi Jumlah Orang Terlantar Di Provinsi Riau Windy Amelia Putri; M. Afdal; Permana, Inggih; Zarnelly
Jurnal Sistem Cerdas Vol. 6 No. 2 (2023)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v6i2.291

Abstract

Displaced people are residents who for some reason cannot meet their needs naturally, both spiritually, physically, and socially. The problem of displaced people occurs for various reasons and urbanization is one of them. Social Service is a government agency that plays a role in improving the quality of social welfare of individuals, groups, and communities. Many community services are carried out by Social Service and one of them is the repatriation of displaced persons. Based on data from the Riau Province Social Service, the number of displaced people in Riau Province from year to year has increased or decreased erratically. This is of course a problem that hinders the Riau Province Social Service in its internal processes such as determining strategies or policies to make decisions. Therefore, this research was conducted to overcome these problems. This research was conducted using the linear regression method with a MAPE result of 7.09% which will be implemented into a prediction application for the number of displaced people and aims to help the Riau Province Social Service get information on the number of displaced people in the next period. Based on the results of the Blackbox test, shows that all menus and features have run very well and obtained a User Acceptance Test (UAT) calculation value of 92%.
Klasifikasi Penerima Bantuan Beras Miskin Menggunakan Algoritma K-NN, NBC dan C4.5 Pristiawati, Andani Putri; Permana, Inggih; Zarnelly, Zarnelly; Muttakin, Fitriani
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): June 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3617

Abstract

One of the tasks of the Dumai City Social Service is to provide poor rice assistance to people in need. The problem that often occurs in the distribution of rice to the poor is that the target recipients of poor rice often occur. In overcoming the existing problems, this research has carried out classification models using the K-Nearest Neighbor (K-NN) algorithm, Naïve Bayes Classifier (NBC), and C4.5 Algorithm. Based on the experimental results, it was found that the best classification model was produced by the K-NN Algorithm with a value of K equal to 21. Besides that, the C4.5 algorithm succeeded in making a decision tree for the classification model with the lowest complexity because it succeeded in reducing the number of attributes from 33 to 5 attributes. The decision tree can be used as material for consideration to the Social Service in making decisions on Raskin beneficiaries.
Peramalan Jumlah Kedatangan Wisatawan Menggunakan Support Vector Regression Berbasis Sliding Window Fitriah, Ma’idatul; Permana, Inggih; Salisah, Febi Nur; Munzir, Medyantiwi Rahmawita; Megawati, Megawati
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7408

Abstract

As a developing city, Pekanbaru has the potential for attractive tourist attractions for tourists. The arrival of tourists has had a big positive impact on the economy of Pekanbaru City. The number of tourist arrivals can experience ups and downs every month, for this reason it is necessary to forecast the number of tourists in the future. This research aims to apply the Orange Data Mining application in predicting the number of tourist arrivals by comparing the kernels in the Support Vector Regression (SVR) method and applying Sliding Window size 3 to window size 13 to transform into time series data. As well as sharing data using the K-Fold Validation method with a value of K-10. Then the performance of the kernels used can be seen using the Test and Score widget which presents the results of Root Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), dan R-squared(R2). The results for forecasting the number of tourist arrivals to Pekanbaru City using the SVR method show that the RBF Kernel is the optimal choice compared to the Polinomial and Linear Kernels. The results of the Test and Score widget show that the RBF Kernel with window size 10 has lower MAE, MSE and RMSE values, namely 0.118, 0.022 and 0.147. Apart from that, the comparison of R2 in window size 10 for Kernel RBF shows better performance with a value of 0.519.
Prediksi Jumlah Bayi Penerima Imunisasi DPT 1 dan DPT 2 Menggunakan Support Vector Regression Idriani R, Nova; Permana, Inggih; Salisah, Febi Nur; Megawati, Megawati; Rahmawita M, Medyantiwi
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7694

Abstract

Vaccination against diphtheria, pertussis (whooping cough), and tetanus is known as DPT immunization, which protects a person from three serious diseases. This vaccine is given in the form of an injection where there are 5 antigens in one injection of the vaccine. DPT immunization is a complete routine immunization that will be continued in grades 1 to 6 elementary school. DPT immunization is feared by mothers because of the side effects that occur in babies after the vaccine injection, namely that the baby will have a fever and be fussy. This has resulted in delays in collecting data on babies who have received this immunization, which has an impact on estimates of babies who will receive DPT immunization in the following month. Of course, this will disrupt the stock of vaccines provided, causing the potential for them to be out of stock. To overcome this problem, it is necessary to collect data on babies who have received DPT in the previous month. This data will be used to predict babies who will receive DPT immunization in the following month using the Support Vector Regression (SVR) method. So that the community health center can provide information regarding the prediction of the number of babies who will receive DPT immunization. This method uses three kernels and a Sliding Window to divide the data into smaller segments, moving alternately across the time series data, making it suitable for predicting babies who will receive DPT immunization in the next time interval. From the three kernels used on the two data that have been separated into DPT 1 and DPT 2, windowing size 3 linear kernels were obtained which were selected as an accurate evaluation of model work on DPT 1 with MAPE values of 3.35, RMSE 0.193, and R2 0.1. And windowing size 3 RBF kernels are more optimal in DPT 2 with MAPE values of 7.86, RMSE 0.163, and R2 0.288.
Analisis Sentimen Masyarakat Mengenai Gerakan Childfree di Media Sosial X Menggunakan Algoritma NBC dan SVM: Sentiment Analysis of Childfree Campaign on X Social Media Using NBC and SVM Algorithms Putra, Moh Azlan Shah; Permana, Inggih; Afdal, M.
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 4 No. 4 (2024): MALCOM October 2024
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v4i4.1356

Abstract

Anak merupakan salah satu entitas yang umum dalam membentuk sebuah keluarga, namun dalam beberapa tahun kebelakang muncul pembahasan mengenai childfree. Dengan banyaknya perdebatan pro-kontra mengenai childfree, perlu dilakukannya sentimen analisis terkait isu ini. Penelitian ini bertujuan untuk menganalisis sentimen masyarakat mengenai gerakan childfree di media sosial X menggunakan algoritma Naïve Bayes Classifier (NBC) dan Support Vector Machine (SVM). Sentimen dibagi menjadi 3 kelas yaitu positif, negatif, dan netral. Penelitian ini mengumpulkan data dengan crawling data pada media sosial X dengan keyword childfree. Data yang diperoleh merupakan data teks mentah sehingga dibutuhkan tahap pra proses. Tahap pra proses yang dilakukan adalah tokenizing, case folding, filter stopword, stemming, TF-IDF, dan data balancing. Berdasarkan simulasi, performa algoritma NBC adalah: akurasi = 56,36%, presisi = 56,41%, dan recall = 56,35%, sedangkan performa algoritma SVM adalah: akurasi 76,12%, presisi 76,36%, dan recall 76,13%. Sehingga dapat disimpulkan bahwa SVM memiliki performa yang lebih baik dari pada NBC pada analisis sentimen di penelitian ini.
Co-Authors Ahsyar, Tengku Khairil Al Kiramy, Razanul Alfakhri, Rezky Andaranti, Arifah Fadhila Annisa Ramadhani Arif Marsal Arifin, Abdullah Febi Nur Salisah, Febi Nur Febi Nur Salisah, Febi Nur Salisah Fikri, M. Hayatul Fitriah, Ma’idatul Fitriah, Ma’idatul Idriani R, Nova Ikhsani, Yulia Intan, Sofia Fulvi Jazman, Muhammad Kusuma, Gathot Hanyokro M Afdal M. Afdal M. Afdal Maulana, Rizki Azli Megawati Megawati - Mona Fronita, Mona Muhammad Naufal, Muhammad Mundzir, Mediantiwi Rahmawita Munzir, Medyantiwi Rahmawita Mustakim Mustakim Mutia, Risma Muttakin, Fitriani Nabillah, Putri Nisa', Sayyidatun Nurkholis Nurkholis Priady, Muhamad Ilham Pristiawati, Andani Putri Putra, Moh Azlan Shah Putra, Tandra Adiyatma Rahmawita M, Medyantiwi Rice Novita Rozanda, Nesdi Evrilyan Sabillah, Dian Ayu Salisah, Febi Nur Salisah Salisah, Pebi Nur Susanti, Pingki Muliya Triningsih, Elsa Ula, Walid Alma Windy Amelia Putri Zarnelly Zarnelly Zarnelly