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All Journal Jurnal Ilmiah Rekayasa dan Manajemen Sistem Informasi Jurnal Informatika Jurnal CoreIT Indonesian Journal of Artificial Intelligence and Data Mining Seminar Nasional Teknologi Informasi Komunikasi dan Industri Journal of Economic, Bussines and Accounting (COSTING) Jurnal Sains dan Informatika Jurnal Informatika Universitas Pamulang Jurnal Teknologi Sistem Informasi dan Aplikasi Jurnal Nasional Komputasi dan Teknologi Informasi Krea-TIF: Jurnal Teknik Informatika JURIKOM (Jurnal Riset Komputer) Informatika : Jurnal Informatika, Manajemen dan Komputer JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Progresif: Jurnal Ilmiah Komputer bit-Tech Jurnal Tekinkom (Teknik Informasi dan Komputer) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) TIN: TERAPAN INFORMATIKA NUSANTARA Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer Bulletin of Information Technology (BIT) Jurnal Informatika Teknologi dan Sains (Jinteks) Malcom: Indonesian Journal of Machine Learning and Computer Science Bulletin of Informatics and Data Science
Gusti, Siska Kurnia
Universitas Islam Negeri Sultan Syarif Kasim Riau
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THE INFLUENCE OF MAJOR EXPERTISE COURSES ON ALUMNI EMPLOYMENT USING THE APRIORI METHOD Irsyad (Scopus ID: 57204261647), Muhammad; Iskandar, Iwan; Gusti, Siska Kurnia
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 10, No 2 (2024): December 2024
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/coreit.v10i2.34144

Abstract

The role of alumni in university progress and quality is vital. This study used data from the tracer study application to analyze the relationship between skill courses and alumni employment. The data mining technique of association was employed to find linkages between different parameters. The Apriori algorithm was used to identify patterns that described the relationship between skill courses and alumni employment. The findings revealed that the most sought-after professions by alumni of the Informatics Engineering Study Program were educators, such as teachers and lecturers, with a support value of 18.7692%. Programmers were also in high demand, with a support value of 15.3846%. The subjects that were found to have the greatest influence on employment were Database, Computer Network, Computer Human Interaction, and Software Engineering. These findings provide valuable insights for the Informatics Engineering Study Program to prioritize and enhance these influential courses in terms of curriculum, teaching methods, and teaching materials, with the aim of improving the relevancy and quality of the courses in supporting alumni employment.
Implementation of Feature Selection Information Gain in Support Vector Machine Method for Stroke Disease Classification Fitri, Anisa; Afrianty, Iis; Budianita, Elvia; Kurnia Gusti, Siska
Bulletin of Informatics and Data Science Vol 4, No 1 (2025): May 2025
Publisher : PDSI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61944/bids.v4i1.116

Abstract

Stroke is a disease with a high mortality and disability rate that requires early detection. However, the main challenge in the classification process of this disease is data imbalance and the large number of irrelevant features in the dataset. This study proposes a combination of Support Vector Machine (SVM) method with Information Gain feature selection technique and data balancing using Synthetic Minority Over-sampling Technique (SMOTE) to improve classification accuracy. The dataset used consists of 5,110 data with 10 variables and 1 label. Feature selection was performed with three threshold values (0.04; 0.01; and 0.0005), while SVM classification was tested on three different kernels: Linear, RBF, and Polynomial. Model evaluation was performed using Confusion Matrix and training and test data sharing using k-fold cross validation with k=10. The best results were obtained on the RBF kernel with Cost=100 and Gamma=5 parameters at an Information Gain threshold of 0.0005, with accuracy reaching 90.51%. These results show that the combination of techniques used aims to determine the variables that most affect SVM classification in detecting stroke disease
Implementation of XGBoost Ensemble and Support Vector Machine For Gender Classification of Skull Bones Ramadhani, Astrid; Afrianty, Iis; Budianita, Elvia; Gusti, Siska Kurnia
Bulletin of Informatics and Data Science Vol 4, No 1 (2025): May 2025
Publisher : PDSI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61944/bids.v4i1.115

Abstract

Sex identification based on skull bones is an important step in forensic anthropology, especially in cases where unidentified human skeletons are found. Conventional methods such as DNA analysis are often used, but have limitations, especially when the bones are damaged, charred or decayed, making the analysis process difficult. This research applies XGBoost ensemble and Support Vector Machine for sex classification on skull bones. The purpose of this research is to handle complex data with many features and unbalanced data using the XGBoost ensemble method and Support Vector Machine (SVM). The data used consisted of 2,524 samples with 82 measurement features. Model performance was evaluated using accuracy, precision, recall, and F1 score metrics. The results showed that the combination of XGBoost and SVM methods, especially with the RBF kernel, was able to achieve accuracy of up to 91.52%. This finding proves that machine learning-based approaches can be an effective and reliable solution in supporting the forensic identification process
Diabetes Classification using Gain Ratio Feature Selection in Support Vector Machine Method Al Rasyid, Nabila; Afrianty, Iis; Budianita, Elvia; Kurnia Gusti, Siska
Bulletin of Informatics and Data Science Vol 4, No 1 (2025): May 2025
Publisher : PDSI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61944/bids.v4i1.114

Abstract

Diabetes is a major cause of many chronic diseases such as visual impairment, stroke and kidney failure. Early detection especially in groups that have a high risk of developing diabetes needs to be done to prevent problems that have a wide impact. Indonesia is ranked seventh in the world with a prevalence of 10.7% of the total number of people with diabetes. This research aims to determine the attributes in the diabetes dataset that most affect the classification and apply the Support Vector Machine method for diabetes classification. For the determination process, Gain Ratio feature selection technique is applied. The dataset used consists of 768 data with 8 attributes. In this classification process, 3 SVM kernels (Linear, Polynomial, and RBF) are used with three possible data divisions using the ratio (70:30; 80:20; 90:10). Before applying feature selection, there were 8 attributes used and achieved the highest accuracy of 94.81% at a ratio of 80:20 using the RBF kernel with a combination of two parameters namely C = 100, Gamma = 3 and C = 100, Gamma = Scale.  Feature selection parameters in the form of thresholds used include 0.02; 0.03; and 0.05. After applying feature selection, the attribute that produces the highest accuracy uses 6 attributes. The highest accuracy after applying feature selection reached 95.45% at a threshold of 0.02 with a ratio of 80:20 using the RBF kernel with parameters C = 100 and Gamma = Scale. The results showed that there was an increase in accuracy after applying feature selection
PENGARUH TEKNIK PENYEIMBANGAN DATA PADA KLASIFIKASI PENYAKIT NAFLD DENGAN ALGORITMA SVM Faska, Ridho Mahardika; Gusti, Siska Kurnia; Budianita, Elvia; Syafria, Fadhilah
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 7 No 2 (2025): EDISI 24
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v7i2.5849

Abstract

Non-Alcoholic Fatty Liver Disease (NAFLD) merupakan penyakit hati kronis yang prevalensinya terus meningkat secara global, termasuk di Indonesia, dengan faktor risiko utama seperti obesitas, diabetes melitus, dan dislipidemia. Deteksi dini NAFLD menjadi tantangan penting karena metode konvensional seperti biopsi hati dan pencitraan memiliki keterbatasan dalam hal biaya, risiko invasif, dan kepraktisan. Penelitian ini bertujuan untuk mengembangkan model klasifikasi NAFLD menggunakan algoritma Support Vector Machine (SVM) dengan memanfaatkan dataset dari Kaggle yang terdiri dari 10 variabel dan 17.549 data. Untuk mengatasi masalah ketidakseimbangan kelas, diterapkan teknik oversampling seperti SMOTE, ADASYN, dan Random Oversampling (ROS) untuk melihat performa akurasi. Hasil penelitian menunjukkan bahwa SMOTE memberikan performa terbaik dengan akurasi tertinggi mencapai 78,70% pada kernel RBF, ROS dengan akurasi 78,18% dan ADASYN dengan akurasi 76,86%. Penelitian ini menyimpulkan bahwa pemilihan teknik oversampling data dan parameter yang tepat sangat penting dalam meningkatkan efektivitas model untuk menangani data tidak seimbang, sehingga dapat berkontribusi pada pengembangan metode deteksi NAFLD yang lebih efisien dan non-invasif.
Clustering Keluarga Miskin Desa Bina Baru dengan Metode K-Medoids Amelia, Felina; Iskandar, Iwan; Gusti, Siska Kurnia; Haerani, Elin; Yusra, Yusra
Krea-TIF: Jurnal Teknik Informatika Vol 11 No 1 (2023)
Publisher : Fakultas Teknik dan Sains, Universitas Ibn Khaldun Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32832/krea-tif.v11i1.14104

Abstract

Kemiskinan di Indonesia terjadi di berbagai daerah, mulai pedesaan hingga perkotaan memiliki permasalahan kemiskinan masing – masing. Masalah kemiskinan juga dialami oleh Desa Bina Baru. Desa Bina Baru yang memiliki jumlah penduduk sebanyak 5.760 jiwa dengan total 1.742 keluarga, yang tersebar dalam 30 Rukun Tetangga (RT) dan 8 Rukun Warga (RW). Upaya dalam penurunan angka kemiskinan dapat dilakukan dengan berbagai cara, mulai pembangunan yang merata, penyaluran bantuan yang tepat sasaran, pemberian kebijakan yang tepat, dan lain sebagainya. Pengelompokan kemiskinan menjadikan salah satu upaya untuk menurunkan angka kemiskinan agar dapat memberikan informasi kepada pemerintahan daerah dalam memberikan kebijakan yang lebih tepat guna. Clustering merupakan teknik data mining yang bertujuan untuk mengelompokkan objek-objek data menjadi beberapa Cluster. Pada penelitian ini pengelompokkan dilakukan dengan teknik pengolahan data mining dengan algoritme K-Medoids dari data Desa Bina Baru tahun 2020 berjumlah 1.005. Hasil perbandingan perhitungan untuk Cluster 1 (kaya) sebanyak 527 penduduk, Cluster 2 (menengah) sebanyak 248 penduduk, dan Cluster 3 (miskin) sebanyak 225 penduduk, Hasil evaluasi dari algoritme k-Medoids adalah 0,991 yang menunjukan cluster yang dibentuk memberikan pengelompokan informasi yang baik. Hasil pengelompokan ini dapat dijadikan acuan untuk informasi kelompok keluarga miskin yang diperlukan pemerintah agar bantuan yang diberikan tepat sasaran.
Pengelompokkan Tingkat Stres Akademik Pada Mahasiswa Menggunakan Algoritma Fuzzy C-Means Alfaiza, Raihan Zia; Budianita, Elvia; Gusti, Siska Kurnia; Afrianty, Iis
TIN: Terapan Informatika Nusantara Vol 6 No 5 (2025): October 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i5.8460

Abstract

Academic stress is a common problem experienced by students due to the burden of assignments, exams, and social pressures. If not managed properly, it can impact achievement and psychological well-being. This study aims to classify the academic stress levels of students at the Faculty of Science and Technology, Sultan Syarif Kasim State Islamic University, Riau, using the Fuzzy C-Means (FCM) algorithm, which allows flexibility in the degree of data membership in more than one cluster. Data were obtained from a modified Perception of Academic Stress Scale (PASS) questionnaire, with 587 respondents from the 2021–2024 intake. The research stages included data selection, cleaning, and transformation, application of the FCM algorithm, and evaluation using three validation metrics: the Partition Coefficient Index (PCI), the Fuzzy Silhouette Index (FSI) and the Silhouette Coefficient. The test results showed the optimal number of clusters at C = 2, with the highest PCI value of 0.5663, FSI and ilhouette Coefficient score of 0.3056, resulting in two groups of students: 313 with high stress levels and 274 with low stress levels. The decrease in PCI, FSI and Silhouette scores across a larger number of clusters indicates that dividing two clusters provides the clearest grouping structure. These findings demonstrate that the FCM algorithm is effective in mapping students' academic stress patterns and can be used as a basis for designing more targeted academic mentoring strategies, counseling services, and psychological intervention programs services.
Penerapan Algortitma C4.5 untuk Klasifikasi Sentimen Masyarakat terhadap #RUUKUHP pada Twitter Vusuvangat, Imam; Kurnia Gusti, Siska; Syafira, Fadhilah; Novriyanto, Novriyanto; Insani, Fitri
Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 6 No. 4 (2023): Jurnal Teknologi Sistem Informasi dan Aplikasi
Publisher : Program Studi Teknik Informatika Universitas Pamulang

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

Abstract

Social media, especially Twitter, has developed into an important tool for people to share their opinions and feelings widely. Users often use hashtags to share messages related to certain topics. Some of the issues that lead to the need for sentiment analysis of the Draft Criminal Code are social impact, Public disapproval, Potential legal uncertainty, Potential abuse, Support and criticism. By conducting a sentiment analysis of the draft Penal Code, the government and policymakers can better understand the views of the public, identify possible problems and address them, and make necessary improvements or clarifications to the draft law. This can help ensure that the draft Penal Code has greater public support and adheres to good legal principles. The classification of public responses to this hashtag provides a significant snapshot of public attitudes and perspectives. This study aims to classify public sentiment towards the RUUKUHP hashtag on the Twitter platform using the C4.5 algorithm. This study uses a collection of tweets with the hashtag RUUKUHP which are manually categorized into two and three sentiment categories, namely positive, negative and positive, negative and neutral. In this study, data preprocessing is carried out before training the model which includes removing links, special characters, removing stopwords, and word tokenization. Furthermore, this research uses text representation methods such as TF-IDF to extract features from the tweet text and convert them into numerical vectors used by the C4.5 algorithm. After training the classification model using the C4.5 algorithm with the classified dataset, it evaluates the performance of the model with the metrics of accuracy, recall, precision, and F1 score. Experimental results using 2 categories of Negative and Positive show that the model applied with the C4.5 algorithm achieved an accuracy of 96.6% with a recall of 96.6%, a percision of 97.1% and an F1 score of 96.8. And experiments using 3 categories of Negative, Positive and Neutral achieved an accuracy of 67%, a recall of 67%, a precision of 65%, and an F1 score of 66%. Thus it can be concluded that the results of the RUUKUHP hashtag sentiment classification with 2 class predictions are more relevant than 3 sentiment class predictions with a value reaching 96.6%.
Thyroid Disease Classification Using Support Vector Machine and Recursive Feature Elimination Method Citra Wulandari; lis Afrianty; Elvia Budianita; Siska Kurnia Gusti
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3454

Abstract

Thyroid disease is a common endocrine disorder that can cause serious metabolic and cardiovascular complications, so accurate early detection is clinically essential. This study proposes a Support Vector Machine (SVM) classifier enhanced with Recursive Feature Elimination (RFE) to select the most informative attributes and Adaptive Synthetic Sampling (ADASYN) to handle class imbalance in a Kaggle thyroid dataset of 3,771 clinical records. The data contain 25 diagnostic attributes with a strongly skewed distribution between healthy and thyroid cases. The model’s robustness was examined using three train–test split ratios. The best configuration, SVM with a Linear kernel and 20 RFE-selected features under an 80:20 split, achieved 98.39% accuracy, with precision, recall, and F1-score all reaching 0.98, indicating consistently strong performance across classes. RFE contributes by removing redundant or weakly relevant variables, helping the classifier construct a more stable and interpretable decision boundary. ADASYN further improves the representation of the minority class, yielding higher recall and F1-score for thyroid cases and reducing the risk of missed diagnoses. Overall, the combined use of feature selection and adaptive oversampling produces a balanced and computationally efficient model for thyroid disease classification. These findings suggest that the proposed approach can support clinical decision-making, reduce diagnostic errors in imbalanced data settings, and strengthen early detection efforts in endocrine health assessment. By offering high sensitivity for thyroid cases while maintaining robust specificity for healthy patients, the model is well suited for integration into clinical decision-support and routine screening workflows.
Klasifikasi Tingkat Keberhasilan Produksi Ayam Broiler di Riau Menggunakan Algoritma K-Nearest Neighbor Beni Basuki; Alwis Nazir; Siska Kurnia Gusti; Lestari Handayani; Iwan Iskandar
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.v4i3.5800

Abstract

Livestock is a crucial component of the Indonesian agriculture sector. One of the most widely practiced types of livestock farming is broiler chicken farming. The production of broiler chickens continues to increase due to the increasing consumption of broiler chickens. Presently, companies are facing an urgent requirement to support farmers, regardless of their level of experience, whether they are newly entering the sector or have been established for some time. Core companies encounter challenges in modeling the success rate of broiler chicken farmer production because of the vast quantity of data coming from collaborating farmers, which makes it arduous for the company to establish the success rate of broiler chicken production. Establishing the level of production success is very helpful in selecting the appropriate farmers to be guided, thus enabling accurate decision-making. A classification procedure utilizing data mining and K-Nearest Neighbor (KNN) algorithm is necessary to manage the growing volume of data. The study examined 927 livestock production data from Riau, where the data was divided into two sets, with 80% allocated for training and the remaining 20% for testing purposes. The findings of the confusion matrix analysis showed that the optimal result was achieved at k = 3, with an accuracy rate of 86.49%, precision of 75.00%, and recall of 70.21%.
Co-Authors Abdul Wahid Abdullah Abdullah Abdullah, Said Noor Abdussalam Al Masykur Adi Mustofa Al Rasyid, Nabila Alfaiza, Raihan Zia Alfin Hernandes Alwaliyanto Alwaliyanto Alwis Nazir Alwis Nazir Alwis Nazir Amelia, Felina Anggi Vasella Azhima, Mohd Baehaqi Beni Basuki Citra Wulandari Cut Lira Kabaatun Nisa Destri Putri Yani Devi Julisca Sari Dina Septiawati Dinyah Fithara efni humairah Eka Pandu Cynthia Eka Pandu Cynthia Elin Haerani Elin Haerani Elin Haerani Elin Haerani Elvia Budianita Erni Rouza, Erni Fadhilah Syafria Fakhri Fakhri Faridatul Jannah Faska, Ridho Mahardika Febi Yanto Fitri Insani Fitri Insani Fitri Wulandari Fitri, Anisa Gusti, Gogor Putra Hafi Puja Iis Afrianty Iis Afrianty Iqbal Salim Thalib Irsyad (Scopus ID: 57204261647), Muhammad Iwan Iskandar Jasril Jasril Jasril Jasril Juliandi Kurniansyah Lestari Handayani lis Afrianty M Wandi Dwi Wirawan Maemonah, Maemonah Morina Lisa Pura Muhammad Affandes Muhammad Affandes Muhammad Fauzan Muhammad Fikry Muhammad Hafiz Muhammad Irsyad Muhammad Khairy Dzaky Muhammad Rifaldo Al Magribi Nada Tsawaabul Khair Nazir, Alwis Norhiza, Fitra Lestari Novriyanto Novriyanto Nurul Ikhsan Okfalisa Okfalisa Pizaini Pizaini Prima Yohana Rahmah Miya Juwita Raja Indra Ramoza Ramadhani, Astrid Risfi Ayu Sandika Robbi Nanda Robby Azhar Salmiyati Salmiyati Sardi, Hajra Satria Bumartaduri Sayyid Muhammad Habib Siti Ramadhani Siti Ramadhani Siti Ramadhani Surya Agustian Suwanto Sanjaya Syafira, Fadhilah Syafria, Fadhillah Syahbudin Hamwar Syaputra, Muhammad Dwiky Teddie Darmizal Umam, Isnaini Hadiyul Vusuvangat, Imam Wan Muhammad Hanif Wulandari, Fitri Yayuk Wulandari yelfi Vitriani Yelfi Yelfi Yola, Melfa Yusra Yusra Yusra Yusra, - Yusra, Yusra