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All Journal TEKNIK INFORMATIKA Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Sistemasi: Jurnal Sistem Informasi Seminar Nasional Teknologi Informasi Komunikasi dan Industri Krea-TIF: Jurnal Teknik Informatika Building of Informatics, Technology and Science Journal of Computer System and Informatics (JoSYC) TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Computer Science and Information Technology (CoSciTech) Bulletin of Computer Science Research JUTECH : Journal Education and Technology Journal of Comprehensive Science Knowbase : International Journal of Knowledge in Database Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Teknik Indonesia INOVTEK Polbeng - Seri Informatika Jurnal Komtika (Komputasi dan Informatika)
Elin Haerani
UIN Suska Riau
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Klasifikasi Penyakit Cacar Monyet Menggunakan Metode Support Vector Machine Anugrah, Wendy; Haerani, Elin; Yusra, Yusra; Oktavia, Lola
Journal of Computer System and Informatics (JoSYC) Vol 5 No 3 (2024): May 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i3.5149

Abstract

Monkey pox is a zoonotic disease caused by the monkey pox virus and this disease is very dangerous. Monkey pox can be detected in advance by using information contained in patient data and applying machine learning techniques. This study aims to classify monkey pox using the Support Vector Machine (SVM) method. This test is carried out using a confusion matrix by comparing the ratio of training data and test data with a ratio of 70:30, 80:20, 90:10 and using the RBF kernel. Based on the test results, the highest ratio results were obtained at 90:10 with the best accuracy value of 65% with SVM parameter testing, namely the value C= 10 and y (gamma)= 1. Based on the results of tests carried out using the Support Vector Machine method, the accuracy values ​​were quite good.
Penerapan K-Means Clustering Pada Data Obat/Alkes di Apotik RSUD Selasih Budianita, Elvia; Haerani, Elin; Nazir, Alwis
Seminar Nasional Teknologi Informasi Komunikasi dan Industri 2023: SNTIKI 15
Publisher : UIN Sultan Syarif Kasim Riau

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

Abstract

Apotik merupakan salah satu tempat yang menjual obat-obatan, alat kesehatan (alkes) dan lainnya. Salah satu faktor penting untuk kelangsungan proses jual beli pada apotik yaitu adanya persediaan obat-obatan. Apotik RSUD Selasih sudah memiliki sistem yang menampung data persediaan obat obatan. Sistem tersebut juga memiliki data transaksi penjualan obat/alkes dan data pasien. Namun, persediaan obat-obatan dilakukan hanya dengan memeriksa persediaan obat yang hampir habis kemudian memperbarui stok persediaan obat tersebut sehingga hal ini kurang efisien jika suatu waktu membutuhkan obat dalam jumlah yang besar dan ternyata stok habis. Pada penelitian ini diterapkan suatu metode data mining K-Means Clustering dengan cara menganalisa pada pemakaian obat untuk menghasilkan informasi yang dapat dijadikan sebagai perencanaan dan pengendalian persediaan obat berdasarkan hasil kluster yang terbentuk. Berdasarkan hasil pengujian yang telah dilakukan menggunakan Davies Bouldin Index, diperoleh jumlah kluster terbaik adalah 2 dengan nilai DBI sebesar 0,33 yaitu kluster yang memiliki permintaan yang tinggi dengan penjualan obat selama 12 bulan diatas 3200 buah dan kluster yang memiliki permintaan yang rendah dengan penjualan obat/alkes selama 12 bulan dibawah 3200 buah.
KLASIFIKASI SENTIMEN MASYARAKAT TERHADAP EFISIENSI ANGGARAN PEMERINTAH MENGGUNAKAN METODE NAÏVE BAYES CLASSIFIER Alfaridzy, M. Audi; Haerani, Elin; -, Jasril; Oktavia, Lola
JUTECH : Journal Education and Technology Vol 6, No 1 (2025): JUTECH JUNI
Publisher : STKIP Persada Khatulistiwa Sintang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31932/jutech.v6i1.4969

Abstract

Kebijakan efisiensi anggaran pemerintah Indonesia tahun 2025 merupakan respons terhadap kebutuhan penguatan fiskal dan pengalokasian ulang anggaran untuk program prioritas nasional. Melalui Instruksi Presiden Nomor 1 Tahun 2025, pemerintah menetapkan penghematan sebesar Rp306,7 triliun dengan memotong belanja kementerian/lembaga dan transfer ke daerah. Meskipun ditujukan untuk mendukung program strategis seperti Makan Bergizi Gratis (MBG), kebijakan ini menimbulkan dampak signifikan, seperti pemangkasan anggaran lembaga penting (misalnya BMKG) lebih dari 50%, pembatalan proyek infrastruktur, serta pengurangan tenaga kerja di sektor media publik. Kondisi ini menimbulkan perdebatan di tengah masyarakat terkait kebutuhan penghematan dan potensi risikonya terhadap pelayanan publik, investasi, serta pemerataan pembangunan. Penelitian ini bertujuan mengklasifikasikan sentimen masyarakat terhadap kebijakan efisiensi anggaran berdasarkan komentar dari media sosial Instagram. Tahapan penelitian meliputi pengumpulan data, pelabelan manual, cleaning, case folding, tokenizing, normalisasi, negation handling, stopword removal, stemming, pembobotan TF-IDF, klasifikasi dengan Naïve Bayes, dan pengujian. Sebanyak 1.408 komentar dari dua akun Instagram diklasifikasikan menggunakan metode Naïve Bayes Classifier dengan hasil akurasi 90,74%, presisi 85,16%, recall 98,51%, dan F1-score 91,35%. Penelitian ini diharapkan dapat dikembangkan dengan metode klasifikasi lainnya di masa depan.
Application of ADASYN Technique in Classification of Stroke Disease using Backpropagation Neural Network zikrillah aulia, said rizki; okfalisa, okfalisa; haerani, elin; oktavia, lola
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/jdhv9s39

Abstract

The high prevalence of stroke in Indonesia and the challenge of imbalanced medical record data are major obstacles to the development of an accurate early detection system. This research aims to build a reliable stroke classification model by applying the ADASYN (Adaptive Synthetic Sampling) oversampling technique to address class imbalance before the data is processed using the Backpropagation Neural Network (BPNN) algorithm. The ADASYN technique is applied with the goal of reducing the bias that arises from the imbalanced data distribution between the majority and minority classes. Testing was conducted through various data splitting scenarios (70:30, 80:20, 90:10) and hyperparameter variations to find the optimal configuration. The best results were obtained with the 90:10 data split scheme, using an architecture of 29 neurons and a learning rate of 0.01, which successfully achieved peak performance with an accuracy of 90.46% and an F1-score of 91.03%. This study demonstrates that the combination of ADASYN and BPNN is a highly effective approach for producing a stroke prediction model that is not only accurate but also sensitive to the minority class, thus having great potential as an early detection support tool in the healthcare sector.
Application of Data Mining for Ceramic Sales Data Association Using Apriori Algorithm Habibi, M. Ilham; Nazir, Alwis; Haerani, Elin; Budianita, Elvia
Knowbase : International Journal of Knowledge in Database Vol. 4 No. 2 (2024): December 2024
Publisher : Universitas Islam Negeri Sjech M. Djamil Djambek Bukittinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30983/knowbase.v5i2.8757

Abstract

This research is conducted to provide an understanding of consumer purchasing patterns at CV. Sukses Bersama by applying data mining using the association rules method and the Apriori algorithm to identify the relationships between one item that influences other items within a ceramic sales dataset at CV. Sukses Bersama. This information is expected to serve as a foundation for improving sales strategies, optimizing customer satisfaction, and expanding the company's market share. The Apriori algorithm is a popular algorithm implemented to identify association rules in data mining. The Apriori algorithm was chosen due to its ability to efficiently identify association rules and its good scalability in handling large datasets. This research begins with the collection of ceramic sales data, followed by data preprocessing to clean and prepare the data. The Apriori algorithm is then applied to discover the association rules, which generate two matrices: support and confidence, and the results are subsequently evaluated. This research was conducted using Google Colaboratory, a web application that is a cloud-based platform provided by Google to run Python code. The results of the study show that the Apriori algorithm can depict significant association structures between different ceramic brand types in the sales data of CV. Sukses Bersama. The calculation results show that the rule has the maximum support and confidence value, namely 67% support value and 84% confidence value in the rule "if you buy the DIAMD brand, you will buy the TOTAL brand"
Klasifikasi Sentimen Presepsi Masyarakat di Instagram Terhadap Paslon Pilpres 2024 Menggunakan Naïve Bayes Classifier (NBC) Akbar, Lionita Asa; Haerani, Elin; Syafria, Fadhilah; Nazir, Alwis; Budianita, Elvia
Jurnal Komtika (Komputasi dan Informatika) Vol 8 No 1 (2024)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v8i1.11293

Abstract

The 2024 presidential election has attracted considerable attention as it has become a controversial issue among the public. Various positive and negative opinions generated can potentially turn into rumors. One of the means used by the public to express their opinions is the social media platform Instagram. Data on public opinions on Instagram can be processed into valuable information through sentiment classification. This research conducted sentiment classification on public perceptions towards the 2024 presidential candidates using a naïve Bayes classifier. The study utilized a dataset consisting of 1000 comments. These comments were collected from several posts on the social media platform Instagram discussing the presidential and vice-presidential candidates. The comments were manually labeled by an expert who is a lecturer in the Indonesian language. Classification was carried out after preprocessing and weighting TF-IDF stages. Based on the research findings, the naïve Bayes classifier method showed an accuracy of 82% and an F1-Score of 83.93% obtained from a 90%:10% split of training and testing data. These results indicate that the naïve Bayes classifier method is effective in classifying the sentiments of the public on Instagram towards the 2024 presidential candidates.
Analisis Algoritma Fuzzy C-Means Untuk Pengelompokan Data Keluarga Cahyadi, Wahyu; Haerani, Elin; Nazir, Alwis; Iskandar, Iwan
TIN: Terapan Informatika Nusantara Vol 6 No 7 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i7.8981

Abstract

Mapping the socio-economic conditions of the community plays a crucial role in supporting targeted development planning at the village level. This study aims to apply the Fuzzy C-Means (FCM) algorithm to cluster families in Bina Baru Village based on social, economic, and household environmental indicators. The variables used include family size, income sources, physical condition of the house, basic facilities, as well as monthly expenditure and income levels. This study uses population data from Bina Baru Village, consisting of 1,000 entries with 16 variables. The FCM algorithm was chosen for its ability to accommodate multiple degrees of membership (fuzzy membership), making it more adaptable in capturing the diversity and ambiguity of socio-economic characteristics. The results show that FCM produces two main clusters: Cluster 0, with 440 members, reflects families with middle to lower economic conditions, permanent housing, and adequate basic facilities; and Cluster 1, with 560 members, represents families with lower economic conditions, semi-permanent housing, and relatively smaller family sizes. Evaluation using the Xie–Beni index (35.4976), Fuzzy Partition Entropy (0.6843), and Fuzzy Cluster Index (0.4468) indicates that the two-cluster model has the best clustering quality compared to other numbers of clusters. Overall, the Fuzzy C-Means algorithm is effective in mapping variations in family welfare and can be used as a basis for formulating development policies and data-driven community empowerment programs in Bina Baru Village.
Klasifikasi Sentimen Bitcoin Terhadap Komentar Di Aplikasi X Menggunakan Metode Decision Tree C4.5 Indrizal, Habibi Putra; Syafria, Fadhilah; Haerani, Elin; Vitriani, Yelvi; Yusra, Yusra
Bulletin of Computer Science Research Vol. 6 No. 1 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i1.932

Abstract

Sentiment analysis is an important method for understanding user perceptions of cryptocurrency assets such as Bitcoin, whose price movements are strongly influenced by public opinion. This study aims to classify user sentiment from comments posted on the X platform into two classes, namely positive and negative, using the Decision Tree C4.5 algorithm. The dataset consists of 5,000 Indonesian-language comments collected through a web scraping process and processed through text preprocessing and TF-IDF–based feature extraction. The model was trained using a 70% training data and 30% testing data split. The evaluation results show that the C4.5 model achieved an accuracy of 78%. For the positive class, the model obtained a very high recall of 0.99 with an F1-score of 0.83, indicating strong performance in identifying positive comments. In contrast, the negative class achieved a recall of 0.51 with an F1-score of 0.67, despite having a high precision of 0.97. The disparity in performance between classes is influenced by the data distribution, which is not fully balanced, with positive comments being more dominant than negative ones, causing the model to be more sensitive to the majority class. Overall, the results indicate that the Decision Tree C4.5 algorithm is sufficiently effective for Indonesian-language Bitcoin sentiment classification, although it still has limitations in recognizing the minority class. Future research may explore the application of data imbalance handling techniques or more advanced algorithms to improve the balance of classification performance across classes.
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.
Implementasi Algoritma Random Forest pada Web-App Sebagai Instrumen Deteksi Dini Penyakit Diabetes Fauzan, Habibul; Haerani, Elin; Kurnia, Fitra; Yanti, Novi
Computer Science and Information Technology Vol 7 No 1 (2026): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v7i1.11261

Abstract

Diabetes is a chronic metabolic disease and one of the leading causes of death worldwide, with the number of sufferers projected to reach 1.3 billion by 2050. Delayed diagnosis remains a primary challenge, as nearly half of those affected are unaware of their condition in the early stages, thereby increasing the risk of fatal complications. Data mining approaches using classification algorithms have been widely utilized for early screening. However, the development of medical record models is often hindered by imbalanced data, which causes models to be biased toward the majority class and reduces detection sensitivity for the minority class (patients with diabetes). Furthermore, there is a lack of research integrating these predictive models into responsive application interfaces for end-users. Consequently, this study implements Random Forest optimized with the SMOTE (Synthetic Minority Over-sampling Technique) into a web-based application to serve as a practical early detection tool. Random Forest was selected for its ability to handle complex data and reduce the risk of overfitting. The research stages include data preprocessing, balancing training data using SMOTE, model parameter adjustment through hyperparameter tuning with Grid Search, and the development of a client-server architecture using AstroJS and Flask. The evaluation results demonstrate that the use of SMOTE significantly improves the model's ability to identify the minority class. The model achieved a Recall of 75.0% and an overall accuracy of 95.8%, effectively minimizing False Negative errors. The developed application was verified through Black Box Testing and was declared successful as a responsive and accessible early detection tool for both healthcare professionals and the general public.
Co-Authors -, Jasril Abdul Haris Kurnia Sandi Harahap Akbar, Lionita Asa Alfaridzy, M. Audi Amelia, Felina Ananda, Rahmadan Alam Ardan Anugrah, Wendy Baehaqi Cahyadi, Wahyu Dede Fadillah Elsilaturrahmi, Elsilaturrahmi Elvia Budianita Fadhilah Syafria Fahmi Rizal Fauzan, Habibul Febi Yanto Fitri Insani Fitri Wulandari Fitria Novitasari Gusti, Gogor Putra Hafi Puja Gusti, Siska Kurnia Habibi, M. Ilham hariansyah, Aldi Ikhsan, Tomi Indrizal, Habibi Putra Israldi, Tino Iwan Iskandar Jasril Jasril Kasman Rukun kurnia, fitra Mai Candra , Reski Maulani, Tedi Muhammad Affandes Muhammad fauzan Muhammad Fikry Muhammad Taufiq Muhammad Yusuf Fadhillah Muhammad, Raja Allifin Nazir, Alwis Novi Yanti Novriyanto Novriyanto Okfalisa Okfalisa Okfalisa Oktavia, Lola Prananda, Alga Pranata, Joni Rahman, Muhammad Taufikur Ramadhani, Siti Surya Agustian Suwanto Sanjaya Syafria, Fadhillah Syah Utama, Isruel Vitriani, Yelvi Yusra, Yusra zikrillah aulia, said rizki