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All Journal TEKNIK INFORMATIKA Syntax Jurnal Informatika Jurnal Ilmu Komputer dan Agri-Informatika SITEKIN: Jurnal Sains, Teknologi dan Industri CESS (Journal of Computer Engineering, System and Science) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Jurnal Informatika Jurnal CoreIT JURNAL MEDIA INFORMATIKA BUDIDARMA Indonesian Journal of Artificial Intelligence and Data Mining Seminar Nasional Teknologi Informasi Komunikasi dan Industri INOVTEK Polbeng - Seri Informatika JURNAL INSTEK (Informatika Sains dan Teknologi) Jurnal Informatika Universitas Pamulang Jurnal Nasional Komputasi dan Teknologi Informasi JURIKOM (Jurnal Riset Komputer) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) JOISIE (Journal Of Information Systems And Informatics Engineering) Building of Informatics, Technology and Science Progresif: Jurnal Ilmiah Komputer bit-Tech Zonasi: Jurnal Sistem Informasi Journal of Applied Engineering and Technological Science (JAETS) Jurnal Tekinkom (Teknik Informasi dan Komputer) JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) Indonesian Journal of Electrical Engineering and Computer Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) JUKI : Jurnal Komputer dan Informatika TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Teknik Informatika (JUTIF) Jurnal Restikom : Riset Teknik Informatika dan Komputer Information System Journal (INFOS) Jurnal Computer Science and Information Technology (CoSciTech) Jurnal UNITEK Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer Jurnal Informatika Teknologi dan Sains (Jinteks) Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Teknik Indonesia Jurnal Informatika: Jurnal Pengembangan IT Jurnal Komtika (Komputasi dan Informatika)
Fadhilah Syafria
Universitas Islam Negeri Sultan Syarif Kasim Riau
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Analisis Sentimen Masyarakat Mengenai Relokasi Penduduk Rempang pada Media Sosial X Menggunakan Metode Naïve Bayes Classifier Taufiq, Muhammad; Haerani, Elin; Syafria, Fadhilah
Jurnal Teknik Indonesia Vol. 4 No. 2 (2025): Jurnal Teknik Indonesia
Publisher : Publica Scientific Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58860/jti.v4i2.711

Abstract

Media sosial X telah menjadi salah satu sarana utama bagi masyarakat dalam menyampaikan opini terhadap isu publik, termasuk kebijakan relokasi penduduk Pulau Rempang sebagai bagian dari Proyek Strategis Nasional (PSN). Permasalahan yang muncul adalah opini publik yang bersifat tidak terstruktur, beragam, dan tersebar luas sulit untuk diklasifikasikan secara manual dan objektif. Oleh karena itu, penelitian ini bertujuan untuk mengembangkan sistem klasifikasi sentimen otomatis terhadap opini masyarakat dengan pendekatan kombinasi leksikal dan pembelajaran mesin. Sebanyak 1.000 tweet relevan dikumpulkan melalui proses crawling dan disaring menggunakan kriteria tertentu. Pelabelan sentimen dilakukan secara otomatis menggunakan InSet Lexicon, sedangkan representasi fitur teks dilakukan dengan metode TF-IDF. Algoritma Naïve Bayes Classifier digunakan sebagai model klasifikasi dan dievaluasi menggunakan confusion matrix, classification report, dan 10-fold cross-validation. Hasil evaluasi menunjukkan bahwa model mampu mengklasifikasikan sentimen pro dan kontra secara efektif, dengan akurasi tertinggi pada data uji sebesar 81,00% (rasio 90:10), dan akurasi validasi silang tertinggi sebesar 80,03% (rasio 80:20). Precision tertinggi diperoleh pada kelas pro (hingga 93%), sedangkan recall tertinggi pada kelas kontra (hingga 89%). Pendekatan ini terbukti efisien dan akurat untuk menganalisis opini publik berbasis media sosial, serta memiliki potensi untuk diterapkan pada isu-isu sosial lainnya yang relevan.
Analisis Clustering Menggunakan Metode K-Means untuk Mengidentifikasi Pola Kepuasan Alumni: Clustering Analysis Using the K-Means Method to Identify Alumni Satisfaction Pattern Ramadhan, Muhammad Ilham; Nazir, Alwis; Irsyad, Muhammad; Sanjaya, Suwanto; Syafria, Fadhilah
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 1 (2026): MALCOM January 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i1.2401

Abstract

Tracer study berperan penting dalam mengevaluasi kualitas layanan pendidikan berdasarkan pengalaman alumni. Analisis kepuasan alumni terhadap fasilitas pembelajaran umumnya masih terbatas pada statistik deskriptif, sehingga belum mampu mengungkap pola kepuasan secara tersegmentasi pada data berskala besar. Penelitian ini bertujuan untuk mengidentifikasi pola segmentasi kepuasan alumni terhadap fasilitas pembelajaran di Universitas Islam Negeri Sultan Syarif Kasim Riau (UIN Suska Riau) sebagai indikator penting dalam evaluasi kualitas layanan pendidikan. Metode yang digunakan adalah K-Means Clustering, diimplementasikan melalui tahapan Knowledge Discovery in Database (KDD) pada 6.508 data tracer study alumni S1 lulusan 2010–2023. Proses preprocessing mencakup normalisasi data numerik menggunakan Min-Max Scaling untuk menyamakan skala enam indikator kepuasan (Perpustakaan, Teknologi Informasi, Modul Belajar, Ruang Belajar, Laboratorium, dan Variasi Mata Kuliah), sehingga meminimalkan bias dalam perhitungan jarak Euclidean. Berdasarkan Elbow Method, diperoleh jumlah klaster optimal adalah K=3, dan kualitas pengelompokan divalidasi dengan nilai Davies-Bouldin Index (DBI) sebesar 0,874, mengonfirmasi stabilitas klaster yang terbentuk. Analisis menghasilkan tiga klaster berbeda: Klaster 0 (Tingkat Kepuasan Tinggi) yang dominan, Klaster 1 (Tingkat Kepuasan Rendah), dan Klaster 2 (Tingkat Kepuasan Sangat Tinggi). Hasil ini memberikan segmentasi kepuasan yang eksplisit sebagai dasar bagi universitas untuk merumuskan strategi peningkatan fasilitas secara terarah dan berkelanjutan.
Sistem Prediksi Produksi Kelapa Sawit Berbasis Gradio Menggunakan Algoritma Regresi Linear Berganda Matondang, Irfan Jamal; Budianita, Elvia; Syafria, Fadhilah; Afrianty, Iis
Bulletin of Computer Science Research Vol. 6 No. 2 (2026): February 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i2.994

Abstract

The instability of oil palm production often leads to discrepancies between production targets and actual outputs, thereby necessitating an accurate prediction model to support operational planning. This study aims to develop an oil palm production prediction model and to identify the most influential variables affecting production outcomes as a basis for data-driven decision-making. The model was developed using the Multiple Linear Regression method based on historical data from 2020–2024, consisting of 60 monthly observations with variables including number of trees, land area, rainfall, number of fruit bunches, and plant age. The research stages included data preprocessing, variable selection through testing several feature combinations, model development, and performance evaluation using the coefficient of determination (R²), Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Square Error (RMSE). The results indicate that the combination of number of trees, land area, number of fruit bunches, and plant age produced the best performance, with an R² value of 0.85 on the training data and 0.81 on the testing data. The MAE values were 125,307 kg and 176,984 kg, the MSE values were 28,870,838,455 kg² and 52,809,954,662 kg², and the RMSE values were 169,914 kg and 229,804 kg, respectively. Based on the regression coefficients, the number of fruit bunches was identified as the most dominant variable, with a coefficient value of 637,720 kg. The model was subsequently implemented using the Python Gradio library in the form of an interactive interface to support production planning effectiveness and minimize the risk of inaccurate decision-making in oil palm plantation management.
Application of Backpropagation Neural Network Using Random Oversampling and Robust Scaler for Classification Thyroid Ummy Agustina Putri; Iis Afrianty; Elvia Budianita; Fadhilah Syafria
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Thyroid disease is a fairly common endocrine disorder that requires rapid and accurate diagnosis so that patients can receive appropriate treatment. This study was conducted to improve the system's ability to classify thyroid disease by utilizing data preprocessing techniques with RobustScaler and Random Over Sampling (ROS), as well as the Backpropagation Neural Network (BPNN) algorithm. The research dataset consisted of 3,771 patient data with 25 clinical attributes describing the condition and function of the thyroid. The data preprocessing process involved data selection, data cleaning, and data transformation using RobustScaler so that each feature had a more stable scale and was not affected by extreme values. The class imbalance problem was overcome using ROS so that the amount of data increased to 6,834 samples and the class distribution became more balanced. The Backpropagation Neural Network algorithm was applied in model training by testing various variations in the number of neurons in the hidden layer (38 and 49) and learning rate (0.01 and 0.001). Training was conducted for 5,000 and 10,000 epochs. Evaluation was performed using the 10-Fold Cross Validation method to obtain more consistent results. The results of the study show that the model is capable of achieving very high accuracy, up to 99.85%, on several parameters. The results show that proper data processing and appropriate parameter selection greatly affect model performance. Overall, the use of RobustScaler and ROS has been proven to significantly improve the accuracy of thyroid disease classification.
Penerapan Data Mining untuk Menentukan Penyebab Kematian di Indonesia Menggunakan Metode Clustering K-Means Lili Rahmawati; Alwis Nazir; Fadhilah Syafria; Elvia Budianita; Lola Oktavia; Ihda Syurfi
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 3 (2023): Maret 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i3.5912

Abstract

Death in medical science is studied in a scientific discipline called tanatology. death is not only experienced by elderly people, but also can be experienced by young people, teenagers, or even babies. Death can be caused by various factors, namely, due to illness, old age, accidents, and so on. Based on information provided by the World Health Organization (WHO), there are five highest causes of death including ischemic heart disease, Alzheimer's, stroke, respiratory disorders, neonatal conditions. In this study, k-means is used to group causes of death in Indonesia based on the number of deaths that occur to determine the cases of death that have the most impact on the high mortality rate in Indonesia. Knowing what these death cases are will provide early preparation in anticipating the causes of death in Indonesia. The purpose of this study was to classify mortality rates based on the number of causes of death which were included in the low, medium, and high clusters by applying the K-Means method. In this study the authors used the K-Means clustering algorithm to classify death rates in data on causes of death in Indonesia from 2017-2021. The results of this study formed 3 clusters which were evaluated using the Davies Bouldin Index (DBI) in Rapidminer with a value of 0.259. Clustering results from a total of 21 cases obtained high, medium and low clusters. This cluster grouping was obtained according to the number of deaths per case, namely the first cluster (C0) was low with 17 cases, the second cluster (C1) was moderate with 3 cases and the third cluster (C2) was high with 1 case.
Analisis Sentimen Tanggapan Masyarakat Terhadap Calon Presiden 2024 Ridwan Kamil Menggunakan Metode Naive Bayes Classifier Neni Sari Putri Juana; Elin Haerani; Fadhilah Syafria; Elvia Budianita
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 4 (2023): Juni 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i4.6168

Abstract

Reaction to public facts about the election of the presidential candidate Ridwan Kamil, which will later be obtained, the data is taken from Twitter based on these problems, it is necessary to do sentiment analysis research. Based on the results of this study, the classification process for the Naïve Bayes Classifier has 3 scenarios for dividing training data and test data, namely with 90%:10% training data, the test data produces an accuary value of 85.43%, a recall value of 100.00%, and a precision of 85.33%. For training data 80%: 20% of the test data produces an accuracy value of 86.38%, a recall of 100.00% and a precision value of 86.38% and for data on the distribution of training data 70%: 30% of the test data produces an accuary value of 84.29 %, 100.00% recall and 84.29% precision. From the tweet data that has been used, there are 1262 positive comments and 242 negative comments. These results prove that the Naïve Bayes classifier is very good for conducting sentiment analysis on Twitter comments about the 2024 presidential candidate Ridwan Kamil. The naïve Bayes classifier process gets the highest accuracy value of 86.38% by dividing the training data 80%:20% test data.
Pemodelan Klasifikasi Untuk Menentukan Penyakit Diabetes dengan Faktor Penyebab Menggunakan Decision Tree C4.5 Pada Wanita Nining Nur Habibah; Alwis Nazir; Iwan Iskandar; Fadhilah Syafria; Lola Oktavia; Ihda Syurfi
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 4 (2023): Juni 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i4.6202

Abstract

Diabetes is closely related to the pancreas, where the pancreas produces the natural hormone insulin, but its function is problematic which causes an increase in blood sugar levels in the body. Rising blood pressure can affect organ function in damaging the function of organs in a person's body such as the kidneys, heart and brain. Where makes a person have a history of diabetes. Diabetes that attacks adults can be prevented through exercise and a regular and healthy diet. According to the International Diabetes Federation (IDF) organization, it is estimated that at least 19.5 million Indonesian people between the ages of 20 and 79 will suffer from diabetes in 2021. China is in first place with diabetes with 140.9 million people. India is next in line with the number of people with diabetes of 74.2 million people. Therefore, early diagnosis is very important because it aims to reduce diabetes and diabetes complications in the future. It is necessary to collect data on patients with diabetes who are expected to be able to do prevention. Therefore applying classification techniques with data mining with the C4.5 algorithm. Where the classification can achieve better accuracy. Algorithm C4.5 is generally used in determining the nodes of a decision tree. Based on the test results, the accuracy is 76.67 percent, the precision is 72 percent, and the recall is 41.67 percent.
Analisis Perbandingan Algoritma C4.5 dan Modified K-Nearest Neighbor (MKNN) untuk Klasifikasi Jamur R. Rahmadhani; Alwis Nazir; Fadhilah Syafria; Liza Afriyanti
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 5 No. 2 (2023): Desember 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i2.7052

Abstract

Mushrooms are organisms that consist of several cells, contain spores, are eukaryotic (have a cell nucleus membrane), and do not have chlorophyll, so fungi depend on other organisms to get food. Mushrooms have very identical shapes, starting with size, shape, smell, and color. So it is difficult for ordinary people to differentiate between poisonous mushrooms and non-poisonous mushrooms. Mistakes in identifying mushrooms can have fatal consequences because they can cause poisoning when consuming mushrooms. Therefore, there is a need for education in classifying poisonous and non-poisonous mushrooms. By applying various classification algorithms, it can be determined which algorithm performs better. In previous research conducted by several researchers on classifying mushrooms, there were differences in the accuracy results for each algorithm. Therefore, this research will raise the question of how to measure or comparion algorithm performance in classification using the C4.5 algorithm and the Modified K-Nearest Neighbor (MKNN) algorithm. The results obtained by comparion the performance of the C4.5 algorithm and the Modified K-Nearest Neighbor (MKNN) algorithm in this research show that the C4.5 algorithm managed to obtain an accuracy level of 98.52%, precision of 98.55%, recall of 98.52%, and f1-score of 98.51%. In contrast, the Modified K-Nearest Neighbor (MKNN) algorithm using the value K=10 achieved an accuracy level of 96.62%, precision of 96.69%, recall of 96.62%, and f1-score value of 96.57%.
Co-Authors Abdul Aziz Abdullah, Said Noor Abdussalam Al Masykur Adrian Maulana Adzhima, Fauzan Agung Syaiful Rahman Agus Buono Agustina, Auliyah Ahmad Paisal Aji Pangestu Adek Akbar, Lionita Asa Alfin Hernandes Alwaliyanto Alwis Nazir Alwis Nazir Alwis Nazir Alwis Nazir Alwis Nazir Alwis Nazir Amalia Hanifah Artya Aminuyati Andre Suarisman Aprima, Muhammad Dzaky Ariq At-Thariq Putra Baehaqi Bib Paruhum Silalahi Boni Iqbal Che Hussin, Ab Razak Darmila Dede Fadillah Deny Ardianto Devi Julisca Sari Dina Septiawati Dodi Efendi Eka Pandu Cynthia Elin Haerani Elin Haerani Elin Haerani Elin Haerani Elin Haerani Elin Haerani Elin Haerani Elin Hearani Ellin Haerani Elvia Budianita Faska, Ridho Mahardika Fatma Hayati Fauzan Adzim Febi Nur Salisah Febi Yanto Felian Nabila Fitra Lestari Fitri Insani Fitri Insani Fitri Wulandari Fratiwi Rahayu Gusrifaris Yuda Alhafis Gusti, Siska Kurnia Guswanti, Widya Habibi Al Rasyid Harpizon Hafez Almirza Hafsyah Hara Novina Putri Harni, Yulia Hertati Ibnu Afdhal Ihda Syurfi Iis Afrianty Iis Afrianty Ikhsan, Tomi Ikhsanul Hamdi Indrizal, Habibi Putra Inggih Permana Irma Sanela Ismail Marzuki Ismar Puadi Isnan Mellian Ramadhan Israldi, Tino Iwan Iskandar Iwan Iskandar Iwan Iskandar Iwan Iskandar Iwan Iskandar Jasril Jasril Jasril Jasril Karina Julita Khair, Nada Tsawaabul Lestari Handayani Lestari Handayani Lili Rahmawati Liza Afriyanti Lola Oktavia Lola Oktavia M Fikry M. Afif Rizky A. Ma'rifah, Laila Alfi Masaugi, Fathan Fanrita Matondang, Irfan Jamal Maulana Junihardi Mawadda Warohma Mazdavilaya, T Kaisyarendika Mhd. Kadarman Mori Hovipah Mori Hovipah Morina Lisa Pura Muhammad Affandes Muhammad Alvin Muhammad Fahri Muhammad Fikry Muhammad Hanif Abdurrohman Muhammad Ichsanul Bukhari Muhammad Irsyad Muhammad Syafriandi, Muhammad Muhammad Taufiq Muhammad Yusril Haffandi Muhammad Yusuf Fadhillah Mulyono, Makmur Muslimin, Al’hadiid Nabyl Alfahrez Ramadhan Amril Nailatul Fadhilah Nazir, Alwis Nazruddin Safaat H Negara, Benny Sukma Neni Sari Putri Juana Nesdi Evrilyan Rozanda Nining Nur Habibah Novriyanto Novriyanto Nurainun Nurainun Okfalisa Okfalisa Permata, Rizkiya Indah Pizaini Pizaini Puspa Melani Almahmuda Putra, Fiqhri Mulianda Putri Mardatillah Putri, Widya Maulida R. Rahmadhani Rahmad Abdillah Rahmad Kurniawan Raja Sultan Firsky Ramadhan, Aweldri Ramadhan, Muhammad Ilham Ramadhani, Siti Reski Mai Candra Reski Mai Candra Reski Mai Candra Reski Mei Candra Riska Yuliana Roni Salambue Said Nanda Saputra Satria Bumartaduri Silfia Silfia Siti Ramadhani Siti Sri Rahayu Suswantia Andriani Suwanto Sanjaya Syaputra, Muhammad Dwiky Teddie Darmizal Ummy Agustina Putri Vitriani, Yelvi Wulandari, Fitri Yaskur Bearly Fernandes Yusra, Yusra Yusril Hidayat Zabihullah, Fayat Zulastri, Zulastri