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All Journal Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Journal of Nonformal Education Scientific Journal of Informatics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer Jurnal Ilmiah Media Sisfo JATI (Jurnal Mahasiswa Teknik Informatika) Indonesian Journal of Electrical Engineering and Computer Science Jurnal Teknik Informatika (JUTIF) Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Informatika dan Rekayasa Komputer Jurnal Manajemen Teknologi dan Sistem Informasi Jurnal Manajemen Sistem Informasi PROCEEDING OF INTERNATIONAL CONFERENCE ON BUSINESS MANAGEMENT AND ACCOUNTING
Jasmir, Jasmir
Teknik Informatika
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Social Sciences

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Analisis dan Perancangan Sistem Informasi Penilaian Kinerja Guru Yayasan Rusyda Medi Andri Medan Takhir, Said Hambali; Jasmir, Jasmir; Sharipuddin, Sharipuddin
Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) Vol. 4 No. 1 (2025): EDISI JANUARI 2025
Publisher : STMIK Triguna Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53513/jursi.v4i1.10573

Abstract

Penelitian ini bertujuan untuk mengembangkan sistem informasi penilaian kinerja guru berbasis web yang diterapkan di Yayasan Rusyda Medi Andri Medan. Sistem ini dirancang untuk memberikan penilaian yang lebih objektif, transparan, dan efisien. Sistem Informasi Penilaian Guru dirancang untuk menghasilkan peringkat kinerja guru secara terstruktur. Proses pengembangan sistem mengikuti metode waterfall yang melibatkan analisis kebutuhan, perancangan sistem, implementasi, dan pengujian menggunakan black-box testing. Sistem berbasis web ini dibangun menggunakan Balsamiq. Hasil implementasi menunjukkan bahwa sistem mampu memberikan rekomendasi penilaian secara lebih akurat dibandingkan metode manual sebelumnya, mengurangi risiko subjektivitas, dan meningkatkan kepercayaan para guru terhadap hasil evaluasi. Sistem ini diharapkan dapat membantu Yayasan Rusyda Medi Andri Medan dalam mengelola proses penilaian kinerja guru secara lebih efektif, mendukung kebijakan promosi dan penghargaan, serta meningkatkan kualitas pendidikan melalui pengembangan profesionalisme tenaga pengajar.
Ekstraksi Fitur untuk Peningkatan Klasifikasi Teks Komentar Video Youtube Spam Menggunakan Deep Learning Jasmir, Jasmir; Riyadi, Willy; Jusia, Pareza Alam
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 6 (2023): December 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i6.5249

Abstract

The proposed algorithms are Bidirectional Long Short Term Memory (BiLSTM) and Conditional Random Fields (CRF) with Data Augmentation Technique (DAT). DAT integrates spam YouTube video comments into the traditional TF-IDF algorithm and generates a weighted word vector. The weighted word vector is fed into BiLSTM CRF to capture context information effectively. The result of this study is a new classification model to spam YouTube comment videos and increase the computational value of its performance. This research conducted two experiments: the first using BiLSTM CRF without DAT and the second using BiLSTM CRF with DAT. The experimental results state that the evaluation score using BiLSTM CRF with DAT shows outstanding performance in text classification, especially in spam YouTube video comment texts, with accuracy = 83.3%, precision = 83.6%, recall = 83.3%, and F-measure = 83.3%. So the combination of the BiLSTM-CRF method and the Data Augmentation Technique is very precise, so it can be used to increase the accuracy of classification texts for spam YouTube video comments
Improving Performance of KNN and C4.5 using Particle Swarm Optimization in Classification of Heart Diseases Jusia, Pareza Alam; Rahim, Abdul; Yani, Herti; Jasmir, Jasmir
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 3 (2024): June 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i3.5710

Abstract

Heart disease is a major problem that must be overcome for human life. In recent years, the volume of medical data related to heart disease has increased rapidly, and various heart disease data has collaborated with information technology such as machine learning to detect, predict, and classify diseases. This research aims to improve the performance of machine learning classification methods, namely K-Nearest Neighbor (KNN) and Decision Tree (C4.5) with particle swarm optimization (PSO) feature in cases of heart disease. In this research, a comparison was made of the performance of the PSO-based K-NN and C4.5 algorithms. Following experiments employing PSO optimization to improve the K-NN and C4.5 algorithms, the findings indicated that the K-NN algorithm performed exceptionally well with PSO, achieving an accuracy of 89.09%, precision of 89.61%, recall of 90.79%, and an AUC value of 0.935.
PENERAPAN DATA MINING DALAM PENGELOMPOKAN SAHAM MELALUI RASIO KEUANGAN PADA SAHAM PAPAN UTAMA BURSA EFEK INDONESIA MENGGUNAKAN METODE K-MEANS CLUSTERING Jumairi, Nopri; Jasmir, Jasmir; Purnama, Benni
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 3 (2025): JATI Vol. 9 No. 3
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i3.13670

Abstract

Investasi saham di Bursa Efek Indonesia kian menarik perhatian, terutama dari kalangan generasi muda. Akan tetapi, rendahnya pemahaman tentang keuangan mengakibatkan banyak investor sulit untuk memilih saham yang tepat, sehingga berisiko menderita kerugian. Salah satu metode untuk mendukung investor dalam proses pengambilan keputusan adalah dengan mengelompokkan saham sesuai dengan rasio keuangan. Studi ini menggunakan metode K-Means Clustering untuk mengelompokkan saham di Papan Utama Bursa Efek Indonesia berdasarkan rasio keuangan, yaitu Price Earning Ratio (PER), Earning Per Share (EPS), Book Value Per Share (BVPS), dan Price to Book Value (PBV). Temuan riset menunjukkan bahwa pendekatan ini mengelompokkan saham ke dalam tiga kategori risiko: Centroid 1 (risiko rendah) memiliki 12 saham, Centroid 2 (risiko sedang) terdiri dari 223 saham, dan Centroid 3 (risiko tinggi) mencakup 3 saham. Perbandingan dengan RapidMiner menampilkan variasi jumlah saham di setiap kategori, dengan hasil pengelompokan yang berbeda. Studi ini menemukan bahwa teknik K-Means Clustering dapat memfasilitasi investor dalam memahami profil risiko saham secara lebih mendalam, yang pada gilirannya mendukung keputusan investasi yang lebih bijaksana dan mengurangi kemungkinan kerugian.
Improving Term Deposit Customer Prediction Using Support Vector Machine with SMOTE and Hyperparameter Tuning in Bank Marketing Campaigns Abidin, Dodo Zaenal; Rosario , Maria; Sadikin , Ali; Nurhadi, Nurhadi; Jasmir, Jasmir
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.3.4585

Abstract

Identifying potential customers for term deposit products remains a challenge in the banking industry due to class imbalance in marketing datasets. This study proposes an integrated approach that combines Support Vector Machine (SVM) with the Synthetic Minority Oversampling Technique (SMOTE) and hyperparameter tuning via GridSearchCV to enhance prediction performance. The dataset comprises 45,211 records containing demographic and campaign-related features. Preprocessing steps include categorical encoding, feature scaling, and SMOTE-based resampling. The optimized SVM model achieves an accuracy of 91% and an AUC of 0.96, outperforming the baseline model and demonstrating strong discriminatory ability, particularly for the minority class. This method improves the balance between precision and recall while reducing bias toward the majority class. The findings confirm the effectiveness of combining SMOTE and SVM for imbalanced classification tasks in the financial domain. These results contribute to the advancement of applied machine learning in informatics, particularly in developing robust decision support systems for data-driven banking strategies. Future work may extend this approach to diverse datasets and explore advanced resampling or ensemble techniques to improve model generalization.
Comparative analysis of word embedding features to improve the performance of deep learning models on social media data Jasmir, Jasmir; Alam Jusia, Pareza; Arvita, Yulia; Gunardi, Gunardi
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.9200

Abstract

In this study, we apply various deep learning methods incorporating word embedding features to evaluate their impact on improving classification performance in sentiment analysis. The methods employed include conditional random field (CRF), bidirectional long short term memory (BLSTM), and convolutional neural network (CNN). Our experiments utilize social media data from restaurant review. By testing different iterations of these deep learning techniques with various word embedding features, we found that the BLSTM algorithm achieved the highest accuracy of 80.00% before integrating word embedding features. After incorporating word embeddings, the BLSTM with the word2vec feature achieved an accuracy of 87.00%. Notably, the CNN showed a significant improvement with the FastText feature. Considering all evaluation metrics—accuracy, precision, recall, and F1-score—the BLSTM algorithm consistently demonstrated the best performance across different word embeddings.
Comparison of robust machine learning algorithms on outliers and imbalanced spam data Abidin, Dodo Zaenal; Jasmir, Jasmir; Rasywir, Errisya; Siswanto, Agus
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i2.pp1130-1144

Abstract

Effective spam detection is essential for data security, user experience, and organizational trust. However, outliers and class imbalance can impact machine learning models for spam classification. Previous studies focused on feature selection and ensemble learning but have not explicitly examined their combined effects. This study evaluates the performance of random forest (RF), gradient boosting (GB), and extreme gradient boosting (XGBoost) under four experimental scenarios: (i) without synthetic minority over-sampling technique (SMOTE) and outliers, (ii) without SMOTE but with outliers, (iii) with SMOTE and without outliers, and (iv) with SMOTE and with outliers. Results show that XGBoost achieves the highest accuracy (96%), an area under the curve-receiver operating characteristic (AUCROC) of 0.9928, and the fastest computation time (0.6184 seconds) under the SMOTE and outlier-free scenario. Additionally, RF attained an AUCROC of 0.9920, while GB achieved 0.9876 but required more processing time. These findings emphasize the need to address class imbalance and outliers in spam detection models. This study contributes to developing more robust spam filtering techniques and provides a benchmark for future improvements. By systematically evaluating these factors, it lays a foundation for designing more effective spam detection frameworks adaptable to real-world imbalanced and noisy data conditions.
Klasifikasi Kelayakan Penerima Beasiswa Menggunakan Naive Bayes dengan Optimasi Atribut Berbasis K-Means Clustering: Classification of Scholarship Eligibility Using Naïve Bayes with Attribute Optimization Based on K-Means Clustering Putri, Azhiah; Jasmir, Jasmir; Purnama, Benni
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 4 (2025): MALCOM October 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i4.2312

Abstract

Penelitian ini bertujuan untuk mengklasifikasikan kelayakan penerima beasiswa di Kecamatan Tabir dengan menggunakan algoritma Naïve Bayes yang dioptimasi melalui K-Means Clustering. Dataset berjumlah 4.155 siswa diproses melalui tahap pra-pemrosesan, mencakup seleksi atribut relevan, pembersihan data, serta transformasi kategori ke bentuk numerik. Proses clustering dilakukan dengan K-Means pada K = 2, 3, dan 5, lalu dievaluasi menggunakan Davies-Bouldin Index (DBI). Hasil terbaik diperoleh pada K = 2 dengan nilai DBI = 0,909, yang selanjutnya digunakan untuk mengelompokkan data menjadi dua klaster, yaitu “Layak” dan “Tidak Layak”. Klaster yang dihasilkan kemudian digunakan untuk mengoptimasi atribut pada tahap klasifikasi menggunakan algoritma Naïve Bayes.. Evaluasi performa menggunakan confusion matrix dengan skema split data 70:30, 80:20, dan 10-fold cross validation. Hasil menunjukkan akurasi masing-masing 96,15%, 96,75%, dan 97,91%; precision 99,90%, 99,85%, dan 99,97%; recall 95,39%, 96,18%, dan 97,47%; serta F1-score 97,60%, 97,97%, dan 98,71%. Berdasarkan hasil tersebut, metode 10-fold cross validation memberikan performa terbaik karena mampu menjaga keseimbangan antara akurasi, precision, recall, dan F1-score. Dengan demikian, integrasi antara K-Means Clustering dan Naïve Bayes terbukti efektif dalam mengoptimasi atribut, serta menghasilkan sistem klasifikasi yang akurat, konsisten, dan andal untuk mendukung keputusan seleksi penerima beasiswa.
Analisis Kepuasan Pengguna Meta AI Instagram Dengan Model TAM-EUCS Trivenna, Priskha; Assegaff, Setiawan; Jasmir, Jasmir
Jurnal Ilmiah Media Sisfo Vol 19 No 2 (2025): Jurnal Ilmiah Media Sisfo
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/mediasisfo.2025.19.2.2503

Abstract

Meta AI is a feature of artificial intelligence created by Meta Inc to assist users in generating content and acquiring information in an interactive manner. This research seeks to assess user satisfaction with the Meta AI feature by employing a combined methodology of the Technology Acceptance Model and End User Computing Satisfaction. The research included 403 participants who actively use Instagram and have utilized the Meta AI feature. The analysis technique employed was Partial Least Squares Structural Equation Modeling to investigate the impact of seven independent variables on user attitude and the effect of user attitude on user satisfaction. The findings indicated that perceived usefulness, perceived ease of use, and format significantly influence user attitude, whereas content, accuracy, technical ease, and timeliness do not show a notable impact. Moreover, user perception significantly impacts user satisfaction. Ultimately, perceived advantages, accessibility, and visual layout are the primary elements influencing user perceptions and contentment regarding AI-driven functionalities on social media.
Penerapan Data Mining Untuk Prediksi Penyakit Diabetes Menggunakan Algoritma C4.5 Zudyanti Dwi Rahma Sari1, Ja Zudyanti Dwi Rahma Sari; arvita, yulia; Jasmir Jasmir
Jurnal Informatika Dan Rekayasa Komputer(JAKAKOM) Vol 4 No 1 (2024): JAKAKOM Vol 4 No 1 APRIL 2024
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/jakakom.2024.4.1.1624

Abstract

Kesehatan merupakan peranan terpenting dalam kehidupan. Salah satu penyakit yang dapat menyebabkan komplikasi dan kematian adalah diabetes. Diabetes merupakan penyakit yang disebabkan oleh pankreas yang tidak memproduksi insulin yang cukup untuk tubuh sehinggan kadar gula dalam darah melebihi normal. Diabetes merupakan penyakit keturunan, penyakit ini dapat diturunkan kepada anaknya dari orang tua yang mengidap penyakit diabetes, sangat disayangkan jika usia yang masih muda sudah mengidap penyakit diabetes. Pemeriksaan dalam dunia medis dilakukan dengan cara pendiagnosaan penyakit berdasarkan gejala-gejala yang dirasakan oleh penderita yang dapat menghasilkan rekam medis gejala penyakit. Untuk meminimalisir angka kematian dari penyakit Diabetes ini, para pakar kesehatan harus melakukan pendiagnosaan penyakit dengan sedini mungkin. Salah satu metode yang dapat digunakan dalam permasalahan ini adalah data mining dengan teknik klasifikasi dengan menggunakan algoritma C4.5. Penelitian ini bertujuan untuk membantu para medis untuk mengklasifikasi para pasien yang memiliki gejala-gejala penyakit diabetes. Algoritma C4.5 merupakan metode yang digunakan untuk klasifikasi yang menghasilkan model berupa pohon keputusan. Pohon keputusan merupakan metode klasifikasi dan prediksi yang terkenal. Algoritma C4.5 merupakan metode yang dapat digunakan untuk memprediksi dan mengetahui nilai akurasi pada pasien dengan gejala-gejala yang diderita pasien apakah pasien tersebut mengidap penyakit Diabetes atau tidak. Berdasarkan hasil pengujian dengan metode cross validation pada tools RapidMiner menggunakan 2 options yaitu 5-Fold Cross Validation yang menghasilkan akurasi 95,88% dan 10-Fold Cross Validation menghasilkan akurasi 95,90%. Yang mana pengujian dengan 10 Fold Cross Validation menghasilkan akurasi yang lebih baik dibandingkan menggunakan 5 Fold Cross Validation. Dengan jumlah kelas data yang sama yaitu pada class positive sebanyak 224 data dan pada class negative sebanyak 140 data. Kata Kunci: Diabetes, Data Mining, Prediksi, Algoritma C4.5, RapidMiner
Co-Authors Abdul Rahim Abidin, Dodo Zaenal Agus Siswanto Agus Siswanto Agustina Arvita, Yulia Ayu, Nur Trisna Fachruddin Fachruddin Faiza Rini Fatima Anggraini Fun, Chi-Win Gunardi Gunardi Hasanah Arif, Surya Hendri Hendri Heri Santoso Herti Yani, Herti Hidayatullah, Arief Irawan Irawan Juliana Juliana Jumairi, Nopri Lasmedi Afuan Lastari, Widya Margaretta S, Chinthya Mega, Meyer Meisak, Despita Muhammad Al-Aziz Sofyan Mulyadi Rusli Nurhadi OSCARIO OSCARIO Pahlevi B, M Riza Pardamean Simanjuntak, Daniel Sintong Pareza Alam Jusia, Pareza Alam Purnama, Benni Putri, Azhiah Raden Mohamad Herdian Bhakti Rasywir, Errisya Rawal Dewa, Suhendra Rohaini, Eni Rosario , Maria Sadikin , Ali Said, Rahaini Mohd Setiawan Assegaf Sharipuddin, Sharipuddin Sidik Praptomo Sophia, Aya Suryawahyuni Latief Takhir, Said Hambali Trivenna, Priskha WIJAYA, IBNU SANI WILLY RIYADI Yudha, M. Zahran Yudi Novianto Zudyanti Dwi Rahma Sari