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All Journal Techno.Com: Jurnal Teknologi Informasi PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Sistemasi: Jurnal Sistem Informasi JURNAL MEDIA INFORMATIKA BUDIDARMA Faktor Exacta CogITo Smart Journal JTERA (Jurnal Teknologi Rekayasa) Jurnal Sisfokom (Sistem Informasi dan Komputer) KACANEGARA Jurnal Pengabdian pada Masyarakat JSiI (Jurnal Sistem Informasi) JURTEKSI CCIT (Creative Communication and Innovative Technology) Journal Jurnal Pendidikan dan Konseling Jurnal Investasi Indonesian Journal of Electrical Engineering and Computer Science Journal of Computer System and Informatics (JoSYC) Madaniya Idealis : Indonesia Journal Information System SKANIKA: Sistem Komputer dan Teknik Informatika Sekretari & Administrasi (Serasi) Jurnal Teknik Informatika (JUTIF) Teknika Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Pendidikan dan Teknologi Indonesia KLIK: Kajian Ilmiah Informatika dan Komputer Decode: Jurnal Pendidikan Teknologi Informasi Bit (Fakultas Teknologi Informasi Universitas Budi Luhur) Jurnal Algoritma Jurnal Ticom: Technology of Information and Communication Telematika MKOM AMMA : Jurnal Pengabdian Masyarakat The Indonesian Journal of Computer Science Jurnal Pengabdian Masyarakat Tekno INOVTEK Polbeng - Seri Informatika Jurnal Pintar Pengabdian dan Pemberdayaan Masyarakat
Triyono, Gandung
Universitas Budi Luhur
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Journal : Jurnal Algoritma

 1 
Penentuan Jumlah Project Implementation Staff dengan Metode Workload Analysis Full Time Equivalent (FTE) dan Analytical Hierarchy Process (AHP) : (Studi Kasus: PT Indodev Niaga Internet) Hakim, Sulaiman; Subekti, Yogi Agung; Triyono, Gandung
Jurnal Algoritma Vol 22 No 1 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-1.2235

Abstract

Dalam suatu proyek yang dikerjakan oleh Tim Implementasi pada PT. Indodev Niaga Internet terkadang Implementation Staff yang ada pada Tim Implementasi sering kali mengalami beban kerja yang berlebih ataupun sebaliknya beban kerja terlalu rendah dengan tidak adanya kesesuaian jumlah Implementation Staff yang terlibat dalam suatu proyek yang dapat mengakibatkan terjadinya ketidakefisienan dalam pengerjaan proyek. Oleh karena itu diperlukan suatu cara untuk mengoptimalkan jumlah Implementation Staff yang ada pada Tim Implementasi agar masalah beban kerja yang berlebih ataupun terlalu rendah dapat teratasi. Cara yang digunakan untuk menyelesaikan masalah tersebut ialah dengan menggunakan Analisis Beban Kerja (Workload Analysis). Analisis Beban Kerja akan mencari kesesuaian jumlah tugas dan beban kerja yang ada dalam suatu organisasi yang akan diekuivalenkan dengan satuan waktu dengan jumlah pegawai yang dimiliki oleh suatu perusahaan dan dikombinasikan dengan Analytical Hierarchy Process (AHP) yang akan menentukan siapa saja Implementation Staff yang tepat dan ideal guna untuk mengisi kekurangan Implementation Staff. 7 kriteria yang telah didapatkan dari hasil kuesioner dan sudah dilakukan pengujian menggunakan Chocran Q-Test ketujuh kriteria yang didapat ialah Tanggung Jawab, Kerjasama Tim, Kemampuan Analisis, Programming Skills, Orientasi terhadap Pencapaian Target, Product Knowledge, dan Kemampuan Berkomunikasi. Dengan menggunakan kedua model tersebut dapat membantu efisiensi Sumber Daya Manusia (Implementation Staff) pada proyek teknologi informasi berbasis layanan.
Analisis Sentimen pada Ulasan Aplikasi Wondr di Play Store dengan Metode Naïve Bayes Nurhikmah, Suci; Ramadani, Romi; Triyono, Gandung
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2507

Abstract

The advancement of digital technology continues to drive innovation in the banking sector, particularly in the development of mobile banking services that are more responsive to customer needs. Bank Negara Indonesia (BNI) has responded to this demand by launching the Wondr application as a replacement for its previous BNI Mobile Banking platform, which has received a wide range of user feedback on the Google Play Store.This study was conducted to understand user opinions and perceptions regarding the Wondr application, with the aim of evaluating feedback that could serve as a strategic basis for enhancing BNI’s digital services. The approach employed sentiment analysis using the Naive Bayes Classifier, implemented in Python. The dataset consisted of 27,124 user reviews.The classification results revealed that 52.9% of the reviews were positive, 39.9% negative, and 7.2% neutral. The Naive Bayes model achieved an accuracy of 82%, although its performance in identifying neutral sentiment remained weak, as evaluated through precision, recall, and F1-Score metrics.These findings indicate that the Wondr application is generally well received by users, although certain aspects still require improvement. The study recommends further exploration of alternative classification algorithms such as Random Forest, Support Vector Machine (SVM), and Deep Learning methodologies, as well as the application of SMOTE techniques to address data imbalance, particularly in neutral sentiment classification.
Prediksi Harga Beras di Kalimantan Barat Menggunakan Metode Regresi Linier Sederhana Azizi, Hibatul; Aris Subagyo, Wismoyo; Triyono, Gandung
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2755

Abstract

Beras merupakan bahan makanan pokok utama masyarakat Indonesia, sehingga kestabilan harga beras menjadi hal yang sangat penting dalam menjaga stabilitas ekonomi, sosial, dan politik. Fluktuasi harga beras yang signifikan, termasuk di Provinsi Kalimantan Barat, sering kali dipengaruhi oleh berbagai faktor, seperti cuaca, produksi, dan distribusi, yang memerlukan pendekatan prediktif untuk mendukung pengambilan keputusan. Penelitian ini bertujuan untuk memprediksi harga beras menggunakan metode regresi linier sederhana, dengan fokus pada harga beras premium dan medium. Data yang digunakan meliputi harga historis beras serta beberapa parameter indeks relevan lainnya. Model regresi linier sederhana diterapkan untuk menganalisis hubungan antara faktor independen dengan harga beras sebagai variabel dependen. Hasil penelitian menunjukkan bahwa model regresi memiliki tingkat akurasi yang sangat baik, dengan nilai Mean Absolute Percentage Error (MAPE) sebesar 2,76% untuk harga beras premium dan 3,28% untuk harga beras medium. Temuan ini menunjukkan bahwa regresi linier sederhana dapat menjadi alat yang andal untuk prediksi harga beras dan mendukung pengambilan keputusan strategis, baik oleh pemerintah maupun pemangku kepentingan lainnya. Model yang dibangun diharapkan dapat berkontribusi terhadap perencanaan kebijakan pangan yang lebih efektif, terutama di wilayah dengan fluktuasi harga tinggi seperti Kalimantan Barat.
Tinjauan Literatur Sistem Rekomendasi Film: Mengidentifikasi Pendekatan Terbaik Febrianti, Rizkia Saski; Ningrum, Sekar Ayu; Triyono, Gandung
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.3011

Abstract

The recommendation system is a crucial element in various digital platforms, particularly within the entertainment industry. Its presence helps users discover films that align with their preferences. As the popularity of digital platforms continues to rise in the modern era, the main challenge lies in meeting users’ needs for relevant recommendations amid the diversity and ever-increasing volume of available content. This study focuses on a literature review to determine the most suitable methods to be applied in movie recommendation systems. The urgency of this research lies in the importance of a platform’s ability to provide recommendations that are not only relevant but also capable of enhancing user engagement and satisfaction. The proposed solution in this study involves applying methods that can analyze user preferences and behavior to improve the accuracy and level of personalization within the recommendation system. The research employs the Systematic Literature Review (SLR) method by collecting articles published between 2020 and 2024 from the Google Scholar database, all of which are relevant to the topic of movie recommendation systems. From the search results, 20 selected articles were used as the basis for analysis. Based on the analysis of these articles, it was found that up until the end of 2024, the most widely used method in movie recommendation systems is Collaborative Filtering, achieving the highest precision rate of 89% and a recall value of 96%.
Model Optimalisasi Seleksi Penerimaan Beasiswa Perguruan Tinggi Swasta Menggunakan K-Means dan TOPSIS Al-akbari, Munawir Fikri; Munandar, Muhamad Arief; Triyono, Gandung
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2531

Abstract

Ensuring a fair and well-targeted scholarship distribution process remains one of the major challenges faced by private universities. In many cases, scholarship recipient selection is carried out subjectively and lacks support from a systematic approach. This study proposes a hybrid method using K-Means Clustering and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to optimize the scholarship selection process. Student data covering academic aspects (GPA), socio-economic factors (parental income and occupation, family dependents), and non-academic components (achievements and organizational activity) were analyzed using the K-Means algorithm to group students with similar characteristics. Silhouette Score validation produced four optimal clusters with a score of 0.1683. Subsequently, the TOPSIS method was applied to rank the clusters based on predetermined criteria. The results show that Cluster 4 achieved the highest ranking with a score of 0.7853, followed by Cluster 3 (0.6359), Cluster 1 (0.6014), and Cluster 2 (0.5807). Attribute contribution analysis revealed that GPA is the dominant factor (48.61%–52.26%), followed by parental income (16.15%–19.59%) and family dependents (11.36%–12.09%). The developed model successfully provides an objective foundation for allocating scholarship quotas based on student group characteristics. This study contributes to the development of a more transparent and accountable scholarship selection system.
Co-Authors - Sumardianto Abdul Hamid Abdurrahman, Faris Nur Achmad Ardiansyah Achmad Solichin Achmad Syarif Adhi, Ajar Parama Aditya Ikhbal Maulana Agus Umar Hamdani Aji Guntoro Al Ghozali, Isnen Hadi Al-akbari, Munawir Fikri Ananda Dian Nugraha Angga Prasetyo Anggita Pamukti Anggraini Ujianti Anwarsyah, Anwarsyah Aris Subagyo, Wismoyo Asep Lukman Arip Hidayat Assegaf , Noval Azizi, Hibatul Chaerul, Muh Coudry Bernadeth Dana Indra Sensuse Daniel Iskandar Dede Wahyu Saputra Dermawan Ginting Devy Fatmawati Dini Astuti Dini Handayani, Dini Djafar, Muhammad Agung A. Djati Kusdiarto Dolly Virgian Shaka Yudha Sakti Dwi Kristanto Dyah Puji Utami Effendi , Muhtar Eliyani, Eliyani Ery Rinaldi Fachrurozy, Achmad Fadel, Muhamad Fahlevi, Noval Fajriah, Riri Febri Maulana Febrianti, Rizkia Saski Feby Lukito Wibowo Firmansyah, Maulana Gilang Ramadhan Hadi rahadian Hafiz, Rahmad Hakim, Sulaiman Hanifa, Annisa Hardjianto, Mardi Helmi Zulqan Hendra Adi Saputra Henny Idam Risnaputra Iman Permana, Iman Indra Indra Jotri Firdani Maharaja Juhari Juhari, Juhari Jumaryadi, Yuwan Kanasfi, Kanasfi Kiki Ari Suwandi kosasih Lestari, Triardani Lis Suryadi Lis Suryadi, Lis Lutfan Lazuardi Luthfi Mawardi Mahendra, M. Azmi Malik Aziz Habibie Maruanaya, Greghar Juan Tjether Maruanaya, Rita Fransina Maskur A, Moch Riyadi Masnuryatie, Masnuryatie Maya Asmita Megananda Hervita P. Melyana, Melyana Mepa Kurniasih MHD. Reza M.I. Pulungan Moch. Rezaf Ivanka Haris Mohammad Aldinugroho Abdullah Muhamad Dikhi Rohman Munandar, Muhamad Arief Muttaqin, Zaenul Ningrum, Sekar Ayu Nurhikmah, Suci Oktiara, Dara Putri Pebry, Fachry Ajiyanda Pirman, Arif Prasetia, Andika Rohman Prasetyo, Angger Totik Rahmat Hidayat Ramadani, Romi Reza Ariftiarno Ridho Firmansyah Ridho Putra Kusmanda Riki Ramdani Saputra Rima Tamara Aldisa Rinto Prasetyo Adi Rizka Pitriyani Rizky Adhi Saputra Rizky Fernanda Aprianto Rizky Tahara Shita Rojakul, Rojakul Rudi Hartono Rudi Hidayat Ryan Prasetya Safrina Amini Septiadi, Septiadi Setyadin, Rahmat Dipo Sittah Ifadah Sri Hartati Sri Melati Subekti, Yogi Agung Sudiyatno Yudi Nugroho Sufyan Asaury, Akhmad Suriah Setiana Widiastuti SURYANI Syarif Hidayatulloh Tansya Ingmukti Taryono, Ono Tunggal Saputra, Tri Aji Umar Alfaruq Umuri, Khairil Utomo Budiyanto Vasthu Imaniar Ivanoti Wahyu Adi Setyo Wibowo Wahyu Cesar, Wahyu Wahyuningram, Nugroho Warih Dwi Cahyo Wawan Gunawan Widyanto, Tetrian Wilsen Grivin Mokodaser Winasis, Reza Handaru Wisanto, Aditya Agus Wisnu Cahyadi Wulan Trisnawati Yasmin , Nadia Yeros Fathullah Achmad Zainal Arifin