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All Journal Techno.Com: Jurnal Teknologi Informasi JOIV : International Journal on Informatics Visualization RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JURNAL MEDIA INFORMATIKA BUDIDARMA JPM (Jurnal Pemberdayaan Masyarakat) ILKOM Jurnal Ilmiah EDUMATIC: Jurnal Pendidikan Informatika Journal of Intelligent Decision Support System (IDSS) Computer Science and Information Technologies Indonesian Journal of Data and Science TIN: TERAPAN INFORMATIKA NUSANTARA Buletin Sistem Informasi dan Teknologi Islam Linier: Literatur Informatika dan Komputer
Herdianti Darwis
Informatics Engineering, Faculty Of Computer Science, Universitas Muslim Indonesia
Aerospace Engineering Arts Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Veterinary Other

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Comparing ECLAT and Decision Tree for Drug Therapy Recommendation Rules on Multi Label Clinical Data Muh Rayhan Fahreza Rayhan; Harlinda; Herdianti Darwis; Roesman Ridwan Raja
Indonesian Journal of Data and Science Vol. 7 No. 2 (2026): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v7i2.410

Abstract

Introduction: Accurate sorting of plastic waste using Resin Identification Codes (RICs) is essential for improving recycling quality. However, conventional deep learning approaches generally require large labeled datasets, which are difficult and costly to collect for small RIC symbols on plastic packaging. Method: This study employed a Few-Shot Learning approach based on Prototypical Networks using a 7-way 5-shot episodic training configuration. A self-collected dataset of 350 images covering seven RIC categories was used, and three backbone architectures, ConvNet4, ResNet-18, and EfficientNet-B2, were compared. An ablation study evaluated support-set augmentation, followed by supervised fine-tuning of the selected model. Results and Discussion: EfficientNet-B2 achieved the highest episodic accuracy of 93.36%, outperforming ResNet-18 at 88.71% and ConvNet4 at 54.14%. EfficientNet-B2 with light augmentation achieved 85.71% accuracy on the fixed 42-image test set. Most errors occurred between visually similar HDPE and PP symbols. Fine-tuning corrected five of six misclassifications, increasing test accuracy to 90.48% and the F1-score from 0.857 to 0.903. Conclusion: Prototypical Networks with an EfficientNet-B2 backbone and cosine distance provide an effective approach for RIC classification under limited-data conditions and offer a practical foundation for automated plastic-waste sorting systems.
Identifying key patterns of college student’s background through exploratory data analysis Sitti Rahmah Jabir; Herdianti Darwis
Journal of Intelligent Decision Support System (IDSS) Vol 9 No 1 (2026): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v9i1.332

Abstract

The declining of student interest had forced universities to examine the characteristics of each student. According to higher education statistics on the number of new students, fluctuating values ​​have been found in recent years. Several research used exploratory data analysis (EDA) approach to analyze new student admissions data. EDA is offered a summary of the dataset analysis and preliminary findings. There are variables decided to be dropped because consisted high number of missing values. On the other hand, some data filled with mean and mode because the number of missing not more than 20%. The missing values in each of attribute might be cleaned using another way. The admission team in university might encourage the registrants to complete and input correct data to the system. Based on the visualization, we found that some college students applied to university from several background of area, demographic and etc. The marketing division might apply another strategy is area had small number of college which is Kalimantan. Public health, computer science and insutry technology are major that have potential to be promoted due to the job prospects.
Analisis Sentimen Masyarakat Terhadap Sistem Pembayaran Mypertamina dengan Metode Random Forest, SVM, dan Naïve Bayes Ayu Amelia; Lilis Nur Hayati; Herdianti Darwis
LINIER: Literatur Informatika dan Komputer Vol 1, No 1 (2024)
Publisher : Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/linier.v1i1.2269

Abstract

PT. Pertamina (PERSERO), sebagai perusahaan BUMN terkemuka di Indonesia di bidang perminyakan, memiliki peran vital dalam pengolahan dan pemasaran minyak bumi, terutama bahan bakar minyak (BBM). Penelitian ini menerapkan tiga metode analisis sentimen yaitu Random Forest, SVM, dan Naïve Bayes untuk mengevaluasi ulasan pengguna terhadap aplikasi MyPertamina. Dengan mengumpulkan data melalui web scraping dari Google Play Store sebanyak 3360 ulasan dianalisis dari 2018 hingga 01 Desember 2023. klasifikasi sentimen terbagi menjadi tiga kategori: positif, negatif, dan netral. Penggunaan Google Colab sebagai alat utama dalam pengolahan data dan implementasi model klasifikasi menawarkan efisiensi dalam eksperimen. Hasil evaluasi menunjukkan bahwa ketiga metode analisis sentimen Random Forest, SVM, dan Naïve Bayes mencapai akurasi tinggi pada evaluasi ulasan aplikasi MyPertamina. Random Forest menonjol dengan akurasi 99.77%, sementara SVM dan Naïve Bayes juga memberikan performa yang baik, masing-masing mencapai 99.31% dan 90.24%. Nilai Precision, Recall, dan F1-Score yang optimal pada ketiga metode mengindikasikan keefektifan mereka dalam menganalisis sentimen ulasan pengguna.
Analisis Sentimen Mental Health Mahasiswa Terhadap Kehidupan Laboratorium di Universitas Muslim Indonesia dengan Pendekatan Naïve Bayes Classifier dan K-Nearest Neighbor Fitri Rahayu; Harlinda Harlinda; Herdianti Darwis
LINIER: Literatur Informatika dan Komputer Vol 3, No 1 (2026)
Publisher : Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/linier.v3i1.3487

Abstract

Mental health adalah kesejahteraan emosional, psikologis, dan sosial seseorang yang berkaitan dengan cara seseorang berpikir, merasa, dan berperilaku, serta kemampuannya mengatasi stres, menjaga hubungan yang sehat, dan menghadapi tantangan secara positif. Aktifitas di laboratorium sering kali memerlukan waktu dan energi yang cukup banyak, yang dapat meningkatkan tekanan pada mahasiswa dan bisa berdampak pada kesehatan mental mereka. Penelitian ini bertujuan menganalisis sentimen mahasiswa terhadap mental health dalam kehidupan laboratorium dengan menggunakan pendekatan Naïve Bayes Classifier dan K-Nearest Neighbor. Penelitian ini menggunakan beberapa teknik pelabelan, yaitu pelabelan manual dan pelabelan menggunkan NLTK, serta pelatihan dengan 5-fold cross-validation dan penggunaan unigram tokenizing. Hasil penelitian menunjukkan bahwa pelabelan manual dengan pendekatan Naïve Bayes Classifier sedikit lebih unggul dengan tingkat akurasi sebesar 95.58%, presisi sebesar 95.60%, dan recall sebesar 95.54% dibandingkan dengan pendekan K-Nearest Neighbor yang menghasilkan tingkat akurasi 91.66%, presisi 91.83% dan recall 91.49%. Sementara itu, pelabelan menggunakan NLTK dengan pendekatan Naïve Bayes Classifier menghasilkan akurasi tertinggi sebesar 94.11%, presisi 94.05%, dan recall 94.22% dibandingkan dengan pendekatan K-Nearest Neighbor yang memiliki akurasi 90.68%, presisi 90.88%, dan recall 91.01%. Dari hasil pengujian, dapat disimpulkan bahwa pendekatan Naïve Bayes Classifier memberikan hasil yang lebih baik dengan mengklasifikasikan sentimen mahasiswa terkait mental health di kehidupan laboratoriom, dengan pelabelan manual memberikan performa terbaik
Ensemble Association Analysis on Kalla Toyota’s Employee Data Using Apriori, FP-Growth, and ECLAT Ahmad Zaky; Lilis Nur Hayati; Herdianti Darwis; Roesman Ridwan Raja
ILKOM Jurnal Ilmiah Vol 18, No 2 (2026)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v18i2.2550.221-236

Abstract

The effective management of employee data plays an important role in supporting organizational decision-making, particularly in today’s data-driven business environment. This study examines the identification of association patterns within employee data at Kalla Toyota by applying a combined approach using the Apriori, ECLAT, and FP-Growth algorithms. The dataset includes information such as demographic characteristics, educational background, marital status, and job classification. Prior to analysis, the data were carefully preprocessed to improve consistency and ensure suitability for pattern discovery. Relevant variables were then selected using statistical measures, including Cramér’s V, Kendall’s Tau, and Chi-Square tests, to capture meaningful relationships among attributes. With a minimum support threshold set at 10%, the combined method produced 84 association rules considered significant. These patterns were further explored using visual tools such as network graphs and matrix plots to better understand the relationships between variables. The findings highlight notable connections among factors such as gender, generational groups, job roles, and marital status. These insights may assist the company in refining its human resource strategies, particularly in areas such as recruitment, employee development, and retention. This study shows that combining multiple association rule techniques can provide a more comprehensive understanding of employee data and support more informed decision-making
Co-Authors - Nurhikma A. Muh. Syafei Emil Ma’arif A. Nurjulianty Abdul Rachman Manga' Adela Regita Azzahra Ahmad Fadly Ahmad Zaky Alfitriana Riska Andi Ranreng Sombeng Annisa Febrianti Arif Kurnia Ayu Amelia Dedy Atmajaya Dewi Widyawati Dian Dolly Indra Dolly Indra Erick Irawadi Alwi Farniwati Fattah Fauzy Fathrurahman Fina Febriana Fitrah Wahyu Ningsih Fitri Rahayu Harlinda Harlinda L Harlinda Lahuddin Haviluddin Haviluddin Hilma Aszahrah Huzain Azis Ihwana As'ad Ihwana As’ad Ikmar Mawardi Irawati Irawati Irawati Jumrayanti Arfah lilis nurhayati Lokapitasari Belluano, Poetri Lestari M. Dimas Taufiqurahman Manga, Abdul Rachman Masita Balla Muh Aristsyah Malik Muh Firman Abd Rahman Muh Rayhan Fahreza Rayhan Muh. Arsyil Karimi Muh. Rifqi Zulkifli Muhammad Dirga Juang Ikhsan Muhammad Faiq Rahmatullah Muhammad Shabran Muhammad Zaqly Luluang Nur Afra Dimitri Pratiwi Nur Almar' Atussaliha Nur Chaerunnisa Purnawansyah Purnawansyah Putri Regina Prayoga Rahma Puspitasari Ramdan Sastra Reyna Aprilia Rahma Roesman Ridwan Raja Selvia Selvia Siska Anraeni Sitti Nurhalimah Sitti Rahmah Jabir Sunarti Passura Backar Suradi Suradi Tanaka Kazuaki Tasrif Hasanuddin Tasrif Hasanuddin Wistiani Astuti Wistiani Astuti Yulita Salim Zahrizhal Ali