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All Journal International Journal of Electrical and Computer Engineering Jurnas Nasional Teknologi dan Sistem Informasi Jurnal Ilmiah KOMPUTASI Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Teknik Komputer AMIK BSI Information System for Educators and Professionals : Journal of Information System Jurnal Penelitian Pendidikan IPA (JPPIPA) Indonesian Journal of Artificial Intelligence and Data Mining Seminar Nasional Teknologi Informasi Komunikasi dan Industri JITK (Jurnal Ilmu Pengetahuan dan Komputer) Sebatik Journal of Information Technology and Computer Engineering JURNAL SIMTIKA (Sistem Informasi dan Informatika) JURTEKSI Informatika : Jurnal Informatika, Manajemen dan Komputer bit-Tech Systematics Jurnal Teknologi Dan Sistem Informasi Bisnis Jurnal Informasi dan Teknologi Jurnal Informatika Ekonomi Bisnis Indonesian Journal of Electrical Engineering and Computer Science Jurnal Teknik Informatika C.I.T. Medicom JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Applied Data Sciences Jurnal Computer Science and Information Technology (CoSciTech) Journal of Applied Computer Science and Technology (JACOST) Majalah Ilmiah UPI YPTK Journal of Computer Scine and Information Technology Bulletin of Computer Science Research Insearch: Information System Research Journal Jurnal Komtekinfo Jurnal Sistim Informasi dan Teknologi INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS : Journal of Information System Innovative: Journal Of Social Science Research Jurnal Teknologi SmartComp Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) JR : Jurnal Responsive Teknik Informatika CSRID Jurnal Responsive Teknik Informatika Methods in Science and Technology Studies

Sumijan

Universitas Putra Indonesia YPTK Padang

Author-ID : 1534814

Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Physics Public Health Social Sciences Other

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Intelligent System for Diagnosing Infectious Diseases in Children Using the Certainty Factor and Naive Bayes Methods Based on Android Ahmad Khomsi; Syafri Arlis; S Sumijan
Sebatik Vol. 30 No. 1 (2026): June 2026
Publisher : STMIK Widya Cipta Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46984/sebatik.v30i1.2743

Infectious diseases in children remain a serious health problem due to their high vulnerability resulting from an immune system that is not yet fully developed. Limited access to medical personnel and delays in early detection often result in ineffective treatment. Therefore, this study aims to design and implement an Android-based intelligent system application capable of detecting infectious diseases in children early on by utilizing the Certainty Factor and Naïve Bayes methods. This system is designed as an expert system that mimics the way pediatricians analyze symptoms and determine preliminary diagnoses. The research methods used include collecting disease and symptom data based on the knowledge of pediatric health experts, data analysis, rule base formation, and the design and implementation of an Android-based system. The Certainty Factor method is used to handle the uncertainty of the level of confidence in the symptoms selected by the user, while the Naïve Bayes method is used to calculate the probability of disease based on historical data. The combination of these two methods aims to improve the accuracy and reliability of diagnostic results. The results of the study show that the developed expert system application is capable of providing initial diagnostic information on infectious diseases in children quickly and easily accessible to parents and health workers. This system is expected to be an effective early detection tool, support initial medical decision-making, and contribute to the development of artificial intelligence-based health technology in Indonesia.

Sentiment Analysis of Public Comments on YouTube Content Using Principal Component Analysis and Naive Bayes Dede Pratama; Sumijan Sumijan; Rini Sovia
Sebatik Vol. 30 No. 1 (2026): June 2026
Publisher : STMIK Widya Cipta Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46984/sebatik.v30i1.2746

The rapid acceleration of digital media development compels public broadcasting institutions to adapt to shifting public information consumption patterns, which are now centered on online platforms. TVRI Sumatera Barat has responded to these dynamics by leveraging YouTube as a channel for content distribution and audience engagement. However, this interaction generates a massive volume of unstructured comment text, rendering manual sentiment analysis inefficient, time-consuming, and prone to subjectivity. This study aims to address these challenges by automatically and objectively classifying user sentiment using a machine learning approach. The applied methodology integrates Principal Component Analysis (PCA) and the Gaussian Naive Bayes algorithm. PCA serves as a dimensionality reduction technique to simplify TF-IDF weighted text features without losing vital information, while Gaussian Naive Bayes was selected for classification due to its efficiency in rapidly processing the continuous numerical data resulting from the PCA transformation. The research dataset comprises 10 comments from the TVRI Sumatera Barat YouTube channel in 2024, collected via the YouTube Data API, which underwent preprocessing and labeling for positive and negative sentiments. Model validation was conducted using a confusion matrix with accuracy, precision, recall, and F1-score metrics. The test results demonstrate that the combination of PCA and Gaussian Naive Bayes effectively enhances computational efficiency and delivers precise classification performance. This research makes a significant contribution by providing a measurable method for public opinion analysis, which is essential as a basis for evaluating audience perception to improve the quality of digital broadcasting strategies in public institutions.

Penerapan Deep Neural Investigation Network (DNIN) Dengan Feature Selection Untuk Prediksi Bencana Banjir Fachrul Ilmawan; Yuhandri Yuhandri; Sumijan Sumijan
Journal of Information System Research (JOSH) Vol 7 No 3 (2026): April 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i3.9321

Floods are natural disasters caused by high rainfall intensity and poor absorption capacity in an area. The impact of floods results in material and human casualties, necessitating a flood disaster mitigation process. Based on this, the purpose of this study is to predict flood disasters with the Deep Learning (DL) concept using the Deep Neural Investigation Network (DNIN) method, which is a CNN–BiLSTM hybrid. The research method used includes Deep Neural Investigation Network (DNIN) combined with feature selection to predict floods. Feature selection is carried out using the SelectKBest method with the ANOVA F-test (f_classif) evaluation function to select features that have the most significant influence on the target flood variable. The DNIN method extracts features from input data and processes the sequence of these features to capture two-way temporal dependencies before being used for prediction. This research dataset consists of 3000 rows of data sourced from Kaggle (https://www.kaggle.com/datasets/yusufginanjar7/banjir-jabodetabek) with fields name_2, name_3, avg_rainfall, max_rainfall, avg_temperature, elevation, landcover_class, ndvi, slope, soil_moisture, year, month banjir, lat long. The results of this study have proven the application of the Deep Neural Investigation Network (DNIN) method with feature selection is able to predict floods. The results show that the application of the DNIN method with feature selection is able to predict flood disasters with an accuracy level of 93%. Based on the results of this study, the application of the Deep Neural Investigation Network (DNIN) method with feature selection is able to provide a significant contribution in predicting flood disasters accurately and can be used as a decision support system in flood disaster risk mitigation and reduction efforts

IDENTIFIKASI AKSARA JAWI PADA NASKAH KUNO PADA CITRA MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK Devi Maryuni; Yuhandri Yuhandri; Sumijan Sumijan
INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS : Journal of Information System Vol 11 No 1 (2026): INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS (Juni 2026)
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Bina Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51211/isbi.v11i1.3832

Permasalahan pelestarian naskah kuno tidak hanya terkait dengan kondisi fisik naskah yang semakin rapuh, tetapi juga dengan keterbatasan sumber daya manusia dalam memahami isi serta aksara yang digunakan, khususnya aksara Jawi atau Arab Melayu. Rendahnya kemampuan masyarakat dalam membaca dan memahami aksara Jawi menjadi hambatan utama dalam mengakses isi naskah secara luas, sehingga berdampak pada terbatasnya pemanfaatan naskah kuno sebagai sumber pengetahuan dan warisan budaya daerah. Penerapan teknologi informasi seperti metode Convolutional Neural Network (CNN) diharapkan mampu mengidentifikasi huruf aksara Jawi sehingga dapat membantu mengatasi keterbatasan kemampuan membaca aksara tersebut serta mendukung proses digitalisasi naskah kuno berbasis citra. Data penelitian diperoleh dari citra naskah kuno beraksara Jawi yang tersimpan di Dinas Kearsipan dan Perpustakaan Provinsi Sumatera Barat, yang selanjutnya digunakan sebagai dataset pelatihan dan pengujian model CNN. Hasil penelitian menunjukkan bahwa model CNN yang dibangun mampu mencapai nilai akurasi sebesar 82,24% dalam mengidentifikasi huruf aksara Jawi. Berdasarkan nilai akurasi tersebut, dapat disimpulkan bahwa metode CNN cukup efektif dalam mengatasi permasalahan keterbatasan pemahaman aksara Jawi dan mampu mengenali pola huruf pada naskah kuno dengan baik. Berdasarkan hasil penelitian, penggunaan CNN diharapkan dapat berdampak pada pelestarian naskah kuno terutama dalam peningkatan aksesibilitas, pelestarian digital, serta pemanfaatan naskah kuno sebagai warisan budaya dan sumber pengetahuan bagi masyarakat luas.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51211/isbi.v11i1.3832

Financial Condition Prediction Using a Soft Voting Ensemble Model Based on Structured Financial Indicators and Unstructured News Data Ibnu Rasyid Munthe; Sumijan Sumijan; Muhammad Tajuddin
Journal of Applied Data Sciences Vol 7, No 3: September 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i3.1417

The rapid growth of capital market participation has increased the need for reliable analytical tools to assess the financial conditions of listed companies. Conventional approaches commonly rely on structured financial indicators and may not fully capture contextual information reflected in financial news. This study aims to evaluate the predictive potential of structured financial data and unstructured textual data using a soft voting ensemble framework. The structured dataset consists of 1,263 financial records collected from the reports of manufacturing companies listed on the Indonesia Stock Exchange, whereas the unstructured dataset contains 6,329 online financial news records related to Indonesian stocks and listed companies. The structured data were processed through missing-value handling, normalization, and class balancing, while the textual data were processed through cleaning, case folding, tokenization, filtering, stemming, and Term Frequency–Inverse Document Frequency (TF-IDF) feature extraction. The proposed ensemble model combines the probability outputs of six base classifiers: Decision Tree, Support Vector Machine, Multinomial Naive Bayes, Logistic Regression, Random Forest, and K-Nearest Neighbors. Experimental results show that the soft voting ensemble achieved an accuracy of 92.66% on the structured dataset and 98.53% on the unstructured dataset. These findings indicate that both financial indicators and textual information can provide valuable predictive signals for identifying company financial conditions. The contribution of this study lies in the comparative evaluation of two distinct data sources using a consistent ensemble-learning framework. However, the two datasets were evaluated independently rather than integrated into a single multimodal pipeline. Therefore, future research should develop a data-fusion mechanism to assess whether combining financial indicators and news-based sentiment can further improve predictive performance and strengthen decision support for investors and financial analysts.

Analisis Pengelompokan Jenis Anomali Aktivitas Pengguna Pada Log Sistem Informasi Klinik Menggunakan Lof Dan K-Means Puja M Alca; Sumijan Sumijan; Rini Sovia
Bulletin of Computer Science Research Vol. 6 No. 4 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i4.972

Digital transformation in the healthcare sector has driven the adoption of clinic information systems for computerized management of patient medical records. Sensitive data security is threatened by user behavior deviations, requiring immediate detection mechanisms. This study aims to identify anomalous activity patterns and indicators from user log records, including unusual database operation frequencies, abnormal access times, and suspicious data manipulation patterns.The Local Outlier Factor algorithm functions to systematically calculate the local density score of each data point relative to its nearest neighbors. This method detects user activities that deviate significantly from normal patterns in daily clinic operational systems. The K-Means Clustering algorithm groups detected anomalous data into clusters based on similarity of user activity feature characteristics. The clustering facilitates administrator categorization of occurring anomaly types along with threat severity levels to the system.Research data were obtained from user activity log records of the clinic information system at Klinik Utama RIDDA Payakumbuh, which underwent preprocessing stages including data cleaning, feature transformation, value normalization, and handling of missing values.Test results demonstrate that the combination of LOF and K-Means achieved accuracy of 89.5%, precision of 87.3%, and recall of 85.7% on the test dataset. These validation metrics prove that the method effectively addresses user behavior deviation detection in the clinic environment. The test results affirm that the hybrid approach can identify suspicious activities with minimal error rates, ensuring reliability. The research contribution provides practical impact for clinic information system administrators in supervising patient data security through integrated early warning mechanisms.

Analisis Komparasi Convolutional Neural Network dan Learning Vector Quantization dalam Klasifikasi Khat Arab Digital Sabri T Rahman; Yuhandri Yuhandri; Sumijan Sumijan
Bulletin of Computer Science Research Vol. 6 No. 4 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i4.976

Arabic khat is a form of writing that possesses complex visual characteristics, such as variations in letter shapes, stroke thickness, texture, and stylistic differences. This complexity creates challenges in manually recognizing different types of khat. This study aims to analyze and compare the performance of Convolutional Neural Network (CNN) and Learning Vector Quantization (LVQ) methods in classifying five types of Arabic khat digital images, namely Diwani, Farsi, Naskh, Ruqaa, and Tuluth. The dataset was obtained from the Kaggle.com platform. CNN architecture consists of an input layer of 100×100×1, followed by two convolutional layers with 32 and 64 filters of size 3×3, each followed by ReLU activation and max pooling with stride 2. The network then includes a fully connected layer with 64 neurons, a final fully connected layer corresponding to the number of classes, a softmax layer, and a classification layer. CNN training was conducted using 5-fold cross-validation, applying data augmentation in each fold. For the LVQ method, Local Binary Pattern (LBP) was used for feature extraction from 100×100 images with parameters: radius 1, 8 neighbors, cell size [48 48], and L2 normalization. The extracted features were used for training with an initialization of 25 prototypes from 5 classes. The process also employed 5-fold cross-validation. From 40 testing samples, the CNN model achieved an accuracy of 87.5%, while the LVQ model achieved an accuracy of 85%. The CNN algorithm demonstrated better performance in handling the complex visual patterns of Arabic khat. Meanwhile, LVQ showed advantages in architectural simplicity and computational efficiency. This research is expected to contribute to the development of Arabic khat image classification systems and serve as a reference in selecting optimal methods for Arabic khat recognition.

Model for identifying high-achieving students using the k-means clustering algorithm and c4.5 classification Kalfinus Waruwu; Gunadi Widi Nurcahyo; Sumijan
Jurnal KomtekInfo Vol. 13 No. 2 (2026): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v13i2.681

Student achievement refers to academic accomplishments or results obtained by students in the field of education, which can influence the process of determining student academic grades, class achievement, and accomplishments. This process plays a strategic role in supporting objective educational decision-making, especially in the preparation of coaching programs, the establishment of awards, and the continuous development of student potential. Based on this, the purpose of this study is to analyze data on high-achieving students using the K-Means and C4.5 algorithms. The research methods used include K-means, which functions to group student data into different groups. C4.5 classification is capable of analyzing data characteristic similarities, and the results of the decision tree are used as the basis for the decision-making process. The dataset in this study consisted of 345 students from SMK Negeri 3 Padangsidimpuan. Based on the results of this study, it was proven that the application of the K-Means and C4.5 algorithms could achieve an accuracy of 98.59%. This research contributes to identifying high-achieving students at SMKN 3 Padangsidimpuan using the K-Means and C4.5 algorithms, which can assist the school in formulating more effective and targeted guidance policies and presenting the results of identifying high-achieving students after clustering and decision tree analysis. This serves as a basis for decision-making in determining student development programs based on objectively identified academic and non-academic achievement clusters.

Co-Authors A Alfarisdon Abdi Rahim Damanik Adek Putri Adi Gunawan Aflili Sari Ahmad Khomsi Ahmad Zaki Aktavera, Beni Alifia Restu Selvanda Amran Sitohang Andre Agasi Andreas Malau Andres Boni Fakio Andri Nofiar Anjun Dermawan Ardia Ovidius Asep Kurniawan Asri Hidayad Ayu Prima Siska Bias Yulisa Geni Billy Hendrik Budi Jaya Budi Permana Putra Caniago, Deosa Putra Cyntia Lasmi Andesti Daeng Saputra Perdana Darma Yunita Darmawi Dede Pratama Dedi Irawan Deosa Putra C aniago Devi Maryuni Devia Kartika Dhena Marichy Putri Dhio Saputra Dian Cyntia Dewi Dina Ayudia Dina Selvia Dinul Akhiyar Dwiki Aulia Fakhri Edo Rinaldi Rais Eka Praja Wiyata Mandala Encik Yoega Renaldi Eri Haryadi Eri Haryadi eriwandi Esa Kurniawan Fachriqi Naldes Fachrul Ilmawan Fajri Karim Fajrul Islami Febri Aldi Febri Hadi Feri Irawan Ghea Paulina Suri Gunadi Widi Nurcahyo Hadi Syahputra Hadrila P A Hafid Dwi Adha Hafiz Mursalan hamsiah hamsiah Hardiansyah Putra Haris Kurniawan Hengki Juliansa Ibnu Rasyid Munthe Ieannoal Vhallah Ilham Roni Yansyah Indra Gunawan Irzal Arif Wisky Iskandar Fitri Jeri Wandana Jufriadif Na`am, Jufriadif Julius Santony Julius Santony Julius Santony Julius Santony K Kadrahman Kalfinus Waruwu Khairul Azmi Lc Granadi Suhaidir Lili Amareza Patriani M Syahputra M. Arif M. Rasyid M.Hafiz Alfansury Mahdiasa Sholihin Mardayulis, Mardayulis Mardison Monsya Juansen Muhammad Hafizh Muhammad Iqbal MUHAMMAD TAJUDDIN Muhammad Tajuddin Muhammad, Abulwafa Mustopa Husein Lubis Nandra Sunaryo Nella Novrina Doni Nindi Misyahdul Yuzi Nopan Pirsa Nur Aini Nurhidayat Okta Veza Pratama, Muhammad Harits Pratiwi, Fitri Puja M Alca Radillah, Teuku Rahmad Dian Rahmi Fauzana Rakhmad Pribowo Hariputra Rani Yunima Astia Rezki - Riadi, Rahadatul ‘Aisy Rian Kurniawan Riski Randa Hidayatullah Roni Roni Roni Salambue Rubiati, Nur Rusnedy, Hidayati S Salmiati Sabri T Rahman Sahyunan Harahap Salsa Fitiansyah Sarjon Defit Seni Oknora Firza Setiawan, Adil Soeheri Soeheri Sofika Enggari Sovia, Rini Sri Amalia Harahap Sri Handayani Subrianto Chandra Suhefi Oktarian Surmayanti, Surmayanti Surya Aulia Rahman Surya Dwi Putra Syafri Arlis Syahid Hakam Abdul Halim Syaljumairi, Raemon Tio Ramadan Sapto Hari Ulia Ulfa Wahyudi Wahid Wardana, Bendra Wendi Boy Widya Febriani Wijaya Hakim Yanto, Musli Yendi Putra Yoga Ananda Putra Yolan Ananda Putri Yosua Ade Pohan Yuhandri Yuhandri, Yuhandri Yuki Saputra Yusma Elda Z Zulvitri ZH, Lina Alfaridah. Zulfitri Yani

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