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All Journal JURIKOM (Jurnal Riset Komputer) bit-Tech JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) JUTECH : Journal Education and Technology JURNAL REKAYASA INFORMASI SWADHARMA (JRIS) Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) JAMAIKA: Jurnal Abdi Masyarakat Abdi Jurnal Publikasi Jurnal Ilmu Komputer (JIK) LOGIC : Jurnal Ilmu Komputer dan Pendidikan Biner : Jurnal Ilmu Komputer, Teknik dan Multimedia OKTAL : Jurnal Ilmu Komputer dan Sains JRIIN :Jurnal Riset Informatika dan Inovasi APPA : Jurnal Pengabdian Kepada Masyarakat Jurnal Intelek Insan Cendikia Riau Jurnal Teknik Informatika JUPIKMAS
Herwis Gultom
Universitas Pamulang
Religion Humanities Astronomy Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Medicine & Pharmacology Social Sciences Transportation Other

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Implentasi Sistem Pendukung Keputusan Menentukan Karyawan Tetap Menggunakan Metode Additive Ratio Assessment (Aras) Herwis Gultom; Rahmat Hidayat
bit-Tech Vol. 7 No. 3 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v7i3.2261

Abstract

Dalam dunia kerja yang semakin kompetitif, meningkatkan kualitas karyawan menjadi salah satu pusat perhatian utama dalam bidang pengembangan sumber daya manusia. Proses penentuan dan pengangkatan seorang karyawan tetap sering kali menghadapi tantangan, terutama terkait dengan kurangnya transparansi dalam penilaian. Saat ini, penilaian karyawan tetap cenderung bersifat subjektif, bergantung pada pendapat pribadi supervisor atau pinpinan, yang dapat mengakibatkan ketidakadilan dan bias dalam pengambilan keputusan. Selain itu, proses perhitungan yang masih dilakukan secara manual membutuhkan waktu yang lama dan dapat menyebabkan kesalahan. Oleh karena itu, diperlukan suatu sistem yang dapat mendukung keputusan dalam menentukan karyawan tetap dengan lebih efisien dan objektif. Salah satu solusi yang disarankan adalah pembuatan Sistem Pendukung Keputusan (SPK) berbasis web yang menggunakan pendekatan Evaluasi Rasio Tambahan. Metode ARAS memungkinkan evaluasi karyawan berdasarkan kriteria yang telah ditentukan secara objektif, sehingga mengurangi pengaruh subjektivitas. Dengan menggunakan pendekatan pengembangan perangkat lunak metode waterfall, sistem ini dirancang untuk memberikan hasil perhitungan yang akurat dan rekomendasi karyawan yang layak untuk diangkat sebagai karyawan tetap. Hasil penelitian menunjukkan bahwa implementasi sistem ini tidak hanya meningkatkan akurasi dalam penilaian, tetapi juga mempercepat proses pengambilan keputusan oleh supervisor. Dengan demikian, sistem ini berpotensi untuk meningkatkan kualitas mengelola sumber daya manusia di perusahaan, menciptakan lingkungan kerja yang lebih adil dan transfaran.
Penerapan Metode Hybrid AHP-TOPSIS Dalam Sistem Pendukung Keputusan Pemilihan Karyawan Terbaik Tsalatsatus Sa’adah; Herwis Gultom
OKTAL : Jurnal Ilmu Komputer dan Sains Vol 5 No 04 (2026): OKTAL : Jurnal Ilmu Komputer Dan Sains
Publisher : CV. Multi Kreasi Media

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Abstract

The selection of the best employees conducted manually and subjectively has the potential to cause calculation errors and unfairness in decision-making. Therefore, a system is needed to provide recommendations that are objective, fast, and accurate. This study aims to develop a web-based Decision Support System (DSS) for the process of selecting the best employees. The method used is a hybrid approach combining the Analytical Hierarchy Process (AHP) to determine criteria weights and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to rank alternatives. The system was tested using actual case data to evaluate the effectiveness of the applied method. The results show that the employee named La Ode Raditya Sambaga achieved the highest preference value of 1.0000 and ranked first. Thus, the web-based hybrid AHP-TOPSIS decision support system is proven to assist management in making decisions more objectively, quickly, and accurately in selecting the best employees.
STUDI KOMPARATIF NAIVE BAYES DAN DECISION TREE PADA DATASET BUNGA IRIS: EVALUASI AKURASI DAN EFISIENSI Daffa Arifta Eryana; Filius Deivivi; Muhammad Ilham Baehaqi; Arya Wicaksono; Zalfa Alykha Puspita; Herwis Gultom
JRIS : Jurnal Rekayasa Informasi Swadharma Vol 5, No 2 (2025): JURNAL JRIS EDISI JULI 2025
Publisher : Institut Teknologi dan Bisnis (ITB) Swadharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56486/jris.vol5no2.898

Abstract

This study presents a comparative analysis between the Naive bayes algorithm and Decision tree algorithms in various domains, including iris species Classification, meat composition analysis, subject Classification, and slope analysis in tourism areas. This study aims to evaluate the performance, advantages, and limitations of both algorithms in various data contexts. The methodology used involves applying both algorithms to various datasets and comparing their accuracy, precision, recall, and F1-score metrics. The results show that both algorithms exhibit competitive performance, with varying strengths depending on the characteristics of the data. Decision trees demonstrate better interpretability and can handle non-linear relationships, while Naive bayes demonstrates strong performance with independent features and large training datasets. These findings contribute to understanding the appropriate application context for each algorithm in data mining tasks.Penelitian ini menyajikan analisis komparatif antara algoritma Naive bayes dan Decision tree di berbagai domain, termasuk klasifikasi spesies iris, analisis komposisi daging, klasifikasi subjek, dan analisis kemiringan di area pariwisata. Penelitian ini bertujuan untuk mengevaluasi kinerja, keunggulan, dan keterbatasan kedua algoritma dalam berbagai konteks data. Metodologi yang digunakan melibatkan penerapan kedua algoritma pada berbagai dataset dan membandingkan akurasi, presisi, recall, serta metrik F1-score. Hasil menunjukkan bahwa kedua algoritma memiliki kinerja yang kompetitif dengan kekuatan yang bervariasi tergantung pada karakteristik data. Decision tree menunjukkan interpretabilitas yang lebih baik dan mampu menangani hubungan non-linear, sedangkan Naive bayes menunjukkan kinerja yang kuat dengan fitur-fitur independen dan dataset pelatihan yang besar. Temuan ini berkontribusi dalam memahami konteks aplikasi yang tepat untuk setiap algoritma dalam tugas data mining.
IMPLEMENTASI MAJALAH DIGITAL SEKOLAH BERBASIS WEBSITE UNTUK MENINGKATKAN LITERASI SISWA SMPN 6 TANGERANG SELATAN Aidil Amal; Kresna Surya Herlambang; Muhamad Fatir Fahreji; Naufal Nur Dzaky; Rendy Ferdiansyah; Rifqi Khumaidi; Rona Nurheni Nadapdap; Vanessa aprillia Diaz; Vebronia Bikolo; Vivi Veronica; Herwis Gultom
Abdi Jurnal Publikasi Vol. 4 No. 3 (2026): Januari
Publisher : Abdi Jurnal Publikasi

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Abstract

Students’ literacy skills are fundamental competencies that support learning activities at the junior high school level. However, low reading interest and limited writing skills remain common challenges in many schools. This study aims to implement a website-based school digital magazine as a literacy medium and analyze its impact on improving students’ literacy. A descriptive approach was employed, with data collected through observation, questionnaires, and documentation. The digital magazine was implemented through the school website as a platform for publishing students’ works and providing digital reading materials. The results indicate that the website-based digital magazine increases students’ engagement in reading and writing activities and receives positive responses toward school literacy programs. Therefore, a website-based digital magazine can be considered a relevant and practical alternative literacy medium for junior high schools.
Analisis Regresi Linear Pengaruh Garis Kemiskinan dan Pengeluaran Kapita terhadap Kemiskinan di Indonesia Ahmad Fahmi Abdillah; Hedwin Winata Halawa; Arsil Mujayanah; Herwis Gultom
Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) Vol. 5 No. 1 (2026): EDISI JANUARI 2026
Publisher : STMIK Triguna Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53513/jursi.v5i1.12378

Abstract

Kemiskinan tetap menjadi isu sosial-ekonomi yang signifikan di Indonesia, terutama di kabupaten/kota Sulawesi Tenggara. Penelitian ini bertujuan untuk menganalisis pengaruh pengeluaran per kapita terhadap persentase penduduk miskin dengan menggunakan metode regresi linier sederhana. Data yang digunakan mencakup pengeluaran per kapita dan persentase penduduk miskin pada 17 kabupaten/kota di wilayah tersebut tahun 2024. Analisis regresi menunjukkan bahwa terdapat hubungan negatif yang signifikan antara pengeluaran per kapita dan tingkat kemiskinan, dengan koefisien regresi sebesar -0,0011 dan R² sebesar 0,596, menandakan bahwa sekitar 59,6% variasi tingkat kemiskinan dapat dijelaskan oleh pengeluaran per kapita. Kabupaten dengan pengeluaran per kapita tinggi, seperti Kendari, memiliki persentase miskin rendah, sementara kabupaten dengan pengeluaran rendah, seperti Konawe Kepulauan, memiliki persentase miskin tertinggi. Hasil ini sejalan dengan teori ekonomi dan memberikan implikasi penting bagi perumusan kebijakan pemerintah daerah, khususnya dalam alokasi anggaran untuk menurunkan kemiskinan. Meskipun analisis menggunakan data hipotetik sebagai demonstrasi metodologi, penelitian ini memberikan dasar bagi studi lanjutan menggunakan data aktual dan variabel sosial-ekonomi tambahan untuk meningkatkan akurasi prediksi.
Pengembangan Sistem Informasi Manajemen Proyek pada PT Solusi Integrasi Multikarya Menggunakan Metode SDLC Waterfall Ruslan Wanandi; Herwis Gultom
OKTAL : Jurnal Ilmu Komputer dan Sains Vol 5 No 03 (2026): OKTAL : Jurnal Ilmu Komputer Dan Sains
Publisher : CV. Multi Kreasi Media

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Abstract

PT Solusi Integrasi Multikarya requires an information system capable of managing projects effectively and in an integrated manner. Currently, project recording, scheduling, and reporting are still performed manually, leading to potential delays and administrative errors. This study aims to design and develop a web-based project management information system to improve efficiency and accuracy in project data management. The system is developed using the Software Development Life Cycle (SDLC) Waterfall model, which includes the stages of requirements analysis, system design, implementation, testing, and maintenance. The development uses PHP programming language, Laravel framework, and MySQL database. The expected result of this study is a project management information system that facilitates project management, monitors work progress, and generates real-time reports, thereby supporting performance improvement and decision-making within the company.
Analisis Keputusan Pembelian Mobil Menggunakan Metode MOORA dalam Sistem Pendukung Keputusan Dede Eko Saputro; Herwis Gultom
Riau Jurnal Teknik Informatika Vol. 4 No. 1 (2025): Maret 2025
Publisher : Prodi Teknik Informatika Universitas Pasir Pengaraian

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30606/rjti.v4i1.3269

Abstract

The purpose of this study is to analyze car purchase decisions using the Multi-Objective Optimization based on Ratio Analysis (MOORA) method. This method was chosen because it is able to handle various conflicting decision-making criteria. The study evaluated fifteen car alternatives based on seven criteria: price, fuel consumption, engine capacity, safety features, comfort, resale value, and CO2 emissions. Price and CO2 emission criteria were considered the most important, while other criteria were considered the most important. The analysis process begins with data collection and the application of criteria for value normalization for each alternative. Then, the final score is obtained by summing the normalization value of the maximized criteria and subtracting the normalization value of the minimized criteria. The results of the analysis show that the car that receives the highest score is the most suitable to buy. This research shows how the MOORA method functions in decision support systems and provides useful knowledge to consumers on how to make more informational and data-driven purchasing decisions.
Predicting Student Academic Performance Using Learning Activity Data: A Comparative Study of Random Forest and Decision Tree Models Rahmat Hidayat; Herwis Gultom; Yuda Samudra
Riau Jurnal Teknik Informatika Vol. 4 No. 3 (2025): November 2025
Publisher : Prodi Teknik Informatika Universitas Pasir Pengaraian

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30606/rjti.v4i3.4032

Abstract

This study compares the effectiveness of the Random Forest and Decision Tree algorithms in predicting students' academic performance based on learning activities. The data used included reading scores, writing scores, math scores, and demographic variables such as gender, race/ethnicity, parental level of education, lunch, and test preparation course. The research was carried out through the stages of data cleaning, training and test data sharing, model training, and evaluation using confusion matrix and accuracy, precision, recall, and F1-score metrics. The results show that Random Forest performs best with 97% accuracy, surpassing Decision Tree which has 94% accuracy. The feature importance analysis  revealed that cognitive ability—especially in the reading score, writing score, and math score features—had the greatest influence on prediction results. These findings confirm that the Random Forest model is more reliable and effective as a prediction tool in the academic decision support system to detect the potential for decline in student achievement early.
Comparative Evaluation of Random Forest and Support Vector Machine for Interpretable Breast Cancer Prediction Herwis Gultom; Indra Kristianto
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3821

Abstract

Breast cancer prediction requires classification models that are not only accurate but also clinically meaningful in minimizing missed malignant cases. This study addresses the research question of whether Random Forest (RF) and Support Vector Machine (SVM) differ meaningfully in sensitivity-oriented breast cancer classification when evaluated under a consistent empirical benchmarking framework. Using the Breast Cancer Wisconsin Diagnostic Dataset from Kaggle, comprising 569 instances and 30 numerical diagnostic features, the study implemented a supervised machine learning workflow involving data cleaning, label encoding, StandardScaler-based feature standardization, stratified 80:20 train–test partitioning, model training, and hyperparameter optimization. Performance was assessed using accuracy, precision, recall, F1-score, confusion matrix analysis, and Area Under the Curve (AUC). The SVM model achieved 97.36% accuracy, 100% precision, 92.85% recall, 96.29% F1-score, and 99.54% AUC, whereas RF achieved 96.49% accuracy, 100% precision, 90.47% recall, 95.00% F1-score, and 99.60% AUC. The primary contribution is therefore positioned as empirical benchmarking rather than a new explainable AI framework. SVM produced fewer false negatives, indicating stronger sensitivity for malignant-case detection at the selected decision threshold, while RF provided complementary feature-importance evidence for identifying influential diagnostic variables. These findings clarify the trade-off between sensitivity-driven predictive reliability and model-specific interpretability, suggesting that SVM is preferable for reducing missed malignant cases, whereas RF remains useful when transparent feature-level insight is required.
Pelatihan Literasi Digital untuk Meningkatkan Kesadaran Internet Sehat Pelajar SMK Bintang Nusantara Herwis Gultom; Chandra Alim; Ahmad Fauzi
Jurnal Pengabdian kepada Masyarakat Indonesia Vol 1 No 3 (2026): April-Juni
Publisher : Samudra Ilmu Indonesia

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Abstract

Kegiatan pengabdian kepada masyarakat ini bertujuan meningkatkan literasi digital dan kesadaran internet sehat pada siswa SMK Bintang Nusantara. Tingginya intensitas penggunaan internet di kalangan pelajar belum sepenuhnya diimbangi pemahaman mengenai etika digital, keamanan data pribadi, dan perilaku bertanggung jawab di ruang digital sehingga berpotensi menimbulkan paparan hoaks, cyberbullying, serta penyalahgunaan data pribadi. Kegiatan menggunakan metode edukasi interaktif dengan pendekatan evaluatif berupa ceramah, diskusi, studi kasus, tanya jawab, kuis, serta pre-test dan post-test, dengan materi mencakup literasi digital, internet sehat, etika berinternet, keamanan data pribadi, hoaks, cyberbullying, dan jejak digital. Kegiatan melibatkan 30 siswa dalam satu pertemuan pada 30 April 2026. Berdasarkan pre-test dan post-test, rerata nilai peserta meningkat dari 61,3 menjadi 83,9 atau naik 22,6 poin (sekitar 36,8%); seluruh peserta yang dinilai meningkat dan ketuntasan (≥75) naik dari 0% menjadi 100%, dengan rerata N-gain 0,59 (kategori sedang). Hasil tersebut menunjukkan peningkatan pemahaman siswa yang terukur mengenai penggunaan internet yang sehat.
Co-Authors Ade Tri Fauzan Adi Febrianto, Adi Agam Multazam Ahmad Afgani Ahmad Fahmi Abdillah Ahmad Fauzi Ahmad Fauzi Aidil Amal Akhiles Salvadore Seina Huler Alim, Chanra Alma Ashofi Alvino Octaviano Andika Fadhilah Anis Mirza Anisa Choirunisya Arkan Abdila Barts Arsil Mujayanah Arya Wicaksono Bilqis, Salsa Sayida Chandra Alim Chandra Alim Daffa Arifta Eryana Daffa Prananda Putra Dava Irba Nusantara Debi Kurnia Deby Ardiansyah Dede Eko Saputro Devi Pratiwi Dimas Indra Jaya Dwiky Darmawan Eldrick Rifaldo Esa Andyarizky Fajri Pramana Putra Faruq Syaifullah Fauzi Ahmad Muda Fiki Maipron Filius Deivivi Haikal Wisnu Fazrian Hedwin Winata Halawa Hendrawan Wibowo Herdiyanto, Oki Ilham Nastiar Indah Lasmini Indra Dwi Aryadi Indra Kristianto Isnanto, Muhamad Hari Jaka Oktavian Khoirul Umam Kresna Surya Herlambang Lingga Safitri Maya Komariah Mogot, Putri Intan Muhamad Alifan Farrizqy Muhamad Donny Alimbara Muhamad Fatir Fahreji Muhamad Hafiz Putra Pratama Muhammad Agil Syahdan Muhammad Arifin Ilham Muhammad Bayu Prasetyo Wibowo Muhammad Fadhil Fathoni Muhammad Fatih Iqbal Muhammad Ilham Baehaqi Muharik, Ricky Narendra Umaro Raisya Naufal Nur Dzaky Ningsih, Purnamawati Pradipa Al-Wafi Putra Prastyo Prayoga Pratama Purnama Djati, Widya Putra Aslam Rasyid Rachmat Triyadi Rahel Azzahra Rahmat Hidayat Ramadhan, Irgi Fahreza Ramadhani, Rido Rendy Ferdiansyah Revina Akbar Ria Ester Rico Ronaldo Rifqi Khumaidi Riki Firmansyah Rio Adriana Rivai Syukur Ramadhan Rona Nurheni Nadapdap Ruslan Wanandi Ryu Kurnianto Putra Sakha, Ghirrid Rakha Ghaly Salsabila, Shifany Setyawan Dwi Prakoso Stephen Alfredo Gultom Sultan Akbar Al Mawar Tamalia Herman, Sandi Tira Tsalatsatus Sa’adah Vanessa Aprillia Diaz Vebronia Bikolo Vivi Veronica Wahyu Ramadhan Yuda Samudra Zalfa Alykha Puspita