Article Per Year (5 Year)
p-Index From 2021 - 2026
7.494
P-Index
This Author published in this journals
All Journal Jurnal Informatika dan Teknik Elektro Terapan JOIV : International Journal on Informatics Visualization International Journal of Artificial Intelligence Research Journal of Information Technology and Computer Science (JOINTECS) Syntax Literate: Jurnal Ilmiah Indonesia JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Conference on Innovation and Application of Science and Technology (CIASTECH) JURTEKSI Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Jusikom: Jurnal Sistem Informasi Ilmu Komputer Jurnal Ilmu Komputer dan Bisnis Systematics Techno Xplore : Jurnal Ilmu Komputer dan Teknologi Informasi Jurnal Teknologi Dan Sistem Informasi Bisnis Buana Ilmu Buana Information Technology and Computer Sciences (BIT and CS) Jurnal Accounting Information System (AIMS) INTERNAL (Information System Journal) International Journal of Educational Review Journal of Applied Data Sciences Jurnal Cahaya Mandalika Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Djtechno: Jurnal Teknologi Informasi KLIK: Kajian Ilmiah Informatika dan Komputer Instal : Jurnal Komputer J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Minfo Polgan (JMP) Abdimas Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer Jurnal Sistem Informasi STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer Innovative: Journal Of Social Science Research Jurnal Accounting Information System (AIMS) INTERNAL (Information System Journal) Jurnal Abdi Citra Jurnal Ilmiah Pengabdian Kepada Masyarakat (Nyiur-Dimas) Informasi interaktif : jurnal informatika dan teknologi informasi Jurnal PETISI (Pendidikan Teknologi Informasi) Journal of Information Technology Jurnal Publikasi Teknik Informatika
Priyatna, Bayu
Universitas Buana Perjuangan Karawang
Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Public Health Social Sciences Other

Published : 70 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 70 Documents
Search

 3   4   5   6   7 
Klasifikasi Sentimen Analisis Ulasan Aplikasi Alfagift Menggunakan Algoritma Long Short Term Memory Juniardi; Tukino; Bayu Priyatna; Shofa Shofia Hilabi
STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer Vol. 4 No. 2 (2025): Mei
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/storage.v4i2.5138

Abstract

Alfagift adalah aplikasi resmi dari Alfamart yang memungkinkan pengguna untuk berbelanja secara online. Berbagai ulasan dan penilaian telah diberikan oleh pengguna. Ulasan merupakan teks yang panjang dan tidak terstruktur. Hal ini dapat menyulitkan pengembang aplikasi Alfagift dalam mencari tau ruang lingkup mana yang harus diperbaiki. Oleh sebab itu, tujuan penelitian ini ialah untuk membuat sebuah model klasifikasi ulasan aplikasi Alfagift yang ada di Google Play Store. Ulasan tersebut akan dibagi menjadi lima kategori yaitu transaksi, sistem, promosi, pelayanan, dan keamanan. Algoritma Long Short Term Memory(LSTM) dengan optimasi Adadelta digunakan untuk mengembangkan model klasifikasi. Hasil dari model tersebut memiliki tingkat accuracy 95% mencerminkan seberapa sering model memberikan prediksi yang tepat secara keseluruhan. Precision 94% menunjukkan bahwa model cukup tepat dalam memberikan label. Recall 93% menunjukan model cukup sensitif dalam mendeteksi ulasan yang seharusnya masuk dalam kategori tersebut. F1-Score 94% menunjukkan model memiliki tingkat keseimbangan yang baik antara kemampuan menangkap ulasan yang benar dan kemampuan menghindari kesalahan prediksi.
Klasterisasi Supplier Berdasarkan Kinerja Menggunakan Algoritma K-Means Afra, Alfina Fadhilah; Hananto, April Lia; Hananto, Agustia; Priyatna, Bayu
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol 7 No 2 (2025): April 2025
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v7i2.1935

Abstract

Untuk memastikan efisiensi operasional dan kualitas produk, evaluasi kinerja supplier sangat berperan penting dalam bidang perindustrian. PT Percetakan masih menggunakan penilaian subjektif dalam menilai kinerja supplier. Variabel dalam data ini yaitu Supplier, Jumlah Pesanan, Harga Satuan dan Rentang Pengiriman yang dapat dianalisis untuk meningkatkan penilaian kinerja supplier. Penelitian ini menggunakan algoritma K-Means untuk mengklasterisasikan supplier berdasarkan kinerja. Algoritma K-Means dipilih karena dapat mengolah data dalam jumlah besar dan efisien, hasil yang didapat lebih objektif dibanding pendekatan subjektif. Hasil Penelitian ini diharapkan dapat memberikan kontribusi dan solusi yang praktis bagi PT Percetakan untuk menentukan mitra kerja sama melalui penerapan algoritma K-Means. Hasil klasterisasi menunjukkan bahwa terdapat perbedaan yang signifikan antara supplier dalam setiap klaster. Supplier dengan performa terbaik (Klaster 1) cenderung memiliki jumlah pesanan yang tinggi, harga satuan lebih kompetitif, dan waktu pengiriman lebih cepat. Sementara itu, supplier dengan performa rendah (Klaster 3) memiliki harga lebih tinggi dan rentang pengiriman yang lama, yang dapat mempengaruhi efisiensi operasional perusahaan.
PENGELOMPOKAN PENJUALAN PRODUK DENGAN MENGGUNAKAN K-MEANS CLUSTERING : STUDI KASUS ANALISIS PENJUALAN COFFEE SHOP OLEH KAGGLE.COM Sifa, Sifa Rismawati; Shofa Shofiah Hilabi; Bayu Priyatna; Agustia Hananto
Jurnal Sistem Informasi (JUSIN) Vol 6 No 1 (2025): Jurnal Sistem Informasi
Publisher : ITB Ahmad Dahlan Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32546/jusin.v6i1.3078

Abstract

This study aims to group sales products in a coffee shop based on transaction data using the K-Means Clustering algorithm. The dataset from Kaggle.com includes the attributes product_id, transaction_qty, and unit_price. This method was chosen because of its ability to identify sales patterns in grouping products into three main clusters including high, medium, and low sales. The research process includes data collection, pre-processing, normalization, determining the optimal number of clusters, to evaluating the results using a Silhouette Score of 0.65. These results indicate that the K-Means method is effective in providing product segmentation that can be used as a basis for making business decisions, in optimizing stock and data-based marketing strategies.
Desain Front End Aplikasi Toko Sepatu Dixstore10 Menggunakan Metode Design Thinking Wahyu Aziz Ramadhani; Bayu Priyatna; Elfina Novalia
Jurnal ilmiah Sistem Informasi dan Ilmu Komputer Vol. 5 No. 2 (2025): Juli : Jurnal ilmiah Sistem Informasi dan Ilmu Komputer
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/juisik.v5i2.1424

Abstract

The objective of this study is to formulate the design of Dixstore10's front-end application interface by applying the design thinking method, which focuses on user needs. Dixstore10 is a local shoe store that supports local products and offers quality shoes to different groups. In this study, the design thinking approach is implemented in five main steps, which form the core of this method. In the "Empathize" phase, surveys and interviews are conducted to further understand user needs. In the next phase, "Define," the main problems to be solved are clearly formulated. This process develops user goals, which form the basis for developing the design structure. "Ideate" visualizes the initial concept through a sitemap, while "Prototype" represents the solution design using HTML, CSS, and JavaScript technologies. The tests were conducted using the System Usability Scale (SUS) method and yielded an average score of 80.3, indicating positive user acceptance of the designed interface. The results of this study not only make an important contribution to the development of the Dixstore10 application, but also serve as a reference for other MSMEs in designing user-centric digital platforms. The iterative approach ensures relevant and functional solutions to support the growth of the MSME sector.
Analisis Klasifikasi Pelanggaran Santri di Pesantren Menggunakan Algoritma Decision Tree Abdullah Abdullah; April Lia Hananto; Shofa Shofia Hilabi; Bayu Priyatna
Jurnal ilmiah Sistem Informasi dan Ilmu Komputer Vol. 5 No. 1 (2025): Jurnal ilmiah Sistem Informasi dan Ilmu Komputer
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/juisik.v5i1.1297

Abstract

Islamic boarding schools (pesantren) are Islamic educational institutions that emphasize discipline in the daily lives of their students. However, the manual recording and evaluation of violations makes it difficult for administrators to conduct systematic analysis. This study aims to apply data mining methods, specifically the Decision Tree algorithm, to analyze student violation data for effective classification. Violation data is grouped into three categories: minor, moderate, and severe violations. A classification model is built through entropy and information gain calculations to determine the best attributes to serve as the initial nodes in the decision tree structure. The attributes used in the model include violation points, violation types, and dormitories. The results show that the Decision Tree algorithm is able to identify violation categories with excellent performance. In the minor category, the model achieved a precision of 0.99, a recall of 1.00, and an f1-score of 1.00. For the moderate category, the model achieved a precision of 0.94, a recall of 1.00, and an f1-score of 0.97, indicating that the model is able to recognize violations with a high level of accuracy and consistency. Meanwhile, in the severe category, the model demonstrated perfect precision of 1.00, recall of 0.87, and f1-score of 0.93. Overall, the model achieved 98% accuracy based on the confusion matrix evaluation, indicating that most of the data was correctly classified. The decision tree visualization also showed that the violation point, type of violation, and dormitory location were the main factors in the classification process. These results demonstrate that a data mining approach can be used to support data-driven decision-making in student guidance.
Klasifikasi Tingkat Penjualan Produk pada Toko Jati Karebet Menggunakan Algoritma Naïve Bayes Setiawan, Revi; Priyatna, Bayu; Novalia, Elfina; Huda, Baenil
JURNAL FASILKOM Vol. 15 No. 2 (2025): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v15i2.9614

Abstract

Penelitian ini bertujuan menerapkan algoritma Naive Bayes untuk mengklasifikasikan tingkat penjualan produk di Toko Jati Karebet selama tahun 2024. Latar belakang penelitian ini adalah belum optimalnya pemanfaatan strategi penjualan berbasis data dalam menentukan prioritas stok dan promosi, yang sering menyebabkan inefisiensi persediaan pada Usaha Mikro, Kecil, dan Menengah (UMKM). Data penjualan historis dianalisis menggunakan pendekatan data mining untuk mengenali pola penjualan dan membangun model prediksi. Tahap awal meliputi preprocessing data, seleksi pesanan yang berstatus selesai, agregasi penjualan per produk, dan pelabelan kategori kelarisan menjadi tiga kelas: laris (>100 unit), kurang laris (20–100 unit), dan tidak laris (<20 unit). Model Gaussian Naive Bayes dilatih dan diuji dengan metode supervised learning menggunakan pembagian data 70% untuk pelatihan dan 30% untuk pengujian. Evaluasi model dilakukan dengan confusion matrix dan metrik klasifikasi. Hasil pengujian menunjukkan akurasi sebesar 76%, dengan precision 0,79, recall 0,98, dan F1-score 0,87 pada kategori laris. Temuan ini membuktikan bahwa Naive Bayes mampu memberikan hasil prediksi yang cukup andal untuk kategori mayoritas, namun kinerjanya menurun pada kategori minoritas akibat ketidakseimbangan distribusi data. Penelitian ini menyimpulkan bahwa algoritma Naive Bayes dapat digunakan sebagai alat bantu pengambilan keputusan dalam manajemen stok dan strategi penjualan UMKM, serta merekomendasikan penerapan teknik penyeimbangan data atau eksplorasi algoritma lain pada penelitian berikutnya untuk meningkatkan performa di semua kategori
SATPOL PP Performance Assessment Using the WASPAS Method in Decision Making Effectiveness Lutfiah, Siti; Priyatna, Bayu; Hananto, April Lia; Novalia, Elfina
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 5 (2024): April 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i5.1739

Abstract

Civil Service Police Unit (SATPOLPP) as one of the government's instruments enforces regional regulations and maintains security and protects the community. Leaders have difficulties when evaluating the performance of their members. Manual performance measurement is very ineffective if carried out randomly or by self-assessment. Performance assessments in local government must follow the procedures or rules applicable in local government regulations. Apart from that, the standard for evaluating honorary staff must be based on assessment criteria. In carrying out the analysis, an effective system is needed that can assess the results of member performance. So a performance assessment decision support system is needed using the Weighted Aggregated Sum Product Assessment (WASPAS) algorithm. The WASPAS method has the ability to solve multi-criteria decision problems which are able to reduce errors and optimize in providing assessments and determining alternative highest and lowest values, speed in data management and provide information output results in the form of reports containing performance assessment ranking results. The weights for each criterion are Absence (20%), Work (40%), Collaboration (10%), Discipline (10%), and Knowledge (20%). The results of manual calculations and the application of the WASPAS method show that the highest alternative value obtained a value of 50.5 to the lowest alternative which obtained a value of 26.5 with the same accuracy. so that the evaluation and sanctions obtained can decide who gets ownership and which members can be recommended to extend the work contract using the criteria for consideration. With this calculation system, it becomes faster and more effective in obtaining performance scores for SATPOLPP members and speeding up the leadership decision-making process.
Penerapan Metode K-Means Clustering Untuk Menentukan Jumlah Penjualan Terlaris Pada CV. Equipment & Tools Julia, Ina; Priyatna, Bayu; Tukino, Tukino; Hilabi, Shofa Shofia
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 13, No 1: April 2024
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v13i1.1840

Abstract

Despite having a large stock, CV Equipment & Tools experienced problems in terms of inadequate archiving of item data, which caused gaps between the items sought and the available data. The current management system is considered inefficient in the company's expenditure on procurement. Sales, purchases and incidental expense data lack proper organization, serving only as archives without actively contributing to marketing progress. This research seeks to assist CV Equipment & Tools management in identifying the most sought-after building materials to increase management efficiency. A system has been designed using K-Means Clustering technique to handle very large data sets. The research findings describe three distinct sales categories: most desirable, moderately desirable, and least desirable. By using the K-Means Clustering method, this problem can be overcome resulting in increased efficiency, proper organization of item data, and uniformity in inventory management at CV Equipment & Tools.Keywords: Sales; Data Mining; K-Means Clustering AbstrakMeskipun memiliki stok yang besar, CV Equipment & Tools mengalami masalah dalam hal tidak memadainya pengarsipan data barang, yang menyebabkan kesenjangan antara barang yang dicari dan data yang tersedia. sistem pengelolaan saat ini dianggap tidak efisien dalam pengeluaran perusahaan untuk pengadaan. Data penjualan, pembelian, dan pengeluaran tak terduga tidak memiliki organisasi yang tepat, hanya berfungsi sebagai arsip tanpa berkontribusi aktif untuk kemajuan pemasaran. Penelitian ini berupaya membantu manajemen CV Equipment & Tools dalam mengidentifikasi bahan bangunan yang paling banyak dicari untuk meningkatkan efisiensi manajemen. Sebuah sistem telah dirancang menggunakan teknik K-Means Clustering untuk menangani kumpulan data yang sangat besar. Temuan penelitian menggambarkan tiga kategori penjualan yang berbeda: paling diminati, cukup diminati, dan kurang diminati. Dengan menggunakan metode K-Means Clustering, masalah ini dapat diatasi sehingga menghasilkan peningkatan efisiensi, pengorganisasian data barang yang tepat, dan keseragaman dalam manajemen inventaris di CV Equipment & Tools. 
Penerapan Desain UI/UX Pada Sistem Penjualan Berbasis Web Dengan Metode Desain Thingking Ismail, Dennisa Avriel; Huda, Baenil; Hilabi, Shofa Shofia; Priyatna, Bayu
Innovative: Journal Of Social Science Research Vol. 4 No. 2 (2024): Innovative: Journal Of Social Science Research
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/innovative.v4i2.10016

Abstract

Di era teknologi saat ini, kita mengalami banyak kemudahan yang secara besar-besaran bermanfaat bagi kita. Seiring berjalannya waktu, teknologi informasi berkembang dengan cepat, memberikan keuntungan yang tidak dapat disangkal bagi masyarakat dalam berbagai aspek kehidupan contohnya adalah sistem terkomputerisasi, menjadi ilustrasi dalam ranah sistem penjualan. User interface merupakan apa yang terlihat selama operasi suatu program, sementara pengalaman pengguna mencakup sensasi yang dirasakan oleh pengguna saat berinteraksi dengan program tersebut. Proses-proses dalam metode Design Thinking bertujuan untuk mengidentifikasi masalah dan menentukan kebutuhan pengguna, memungkinkan mereka untuk diatasi dalam konteks UI/UX. Dengan memanfaatkan informasi yang dikumpulkan dari masalah yang diidentifikasi, sebuah antarmuka UI/UX akan dibuat untuk mengatasi dan meningkatkan proses pemesanan. Design thinking berfungsi sebagai salah satu pendekatan untuk menentukan dan merancang antarmuka.
PERANCANGAN EA (EXPER ADVISOR) UNTUK TRADING FOREX DENGAN BAHASA MQL4 Aulia, Aldi; Priyatna, Bayu; Hananto, Agustia; Hananto, April Lia; Tukino, Tukino
Jurnal Cahaya Mandalika ISSN 2721-4796 (online) Vol. 4 No. 2 (2023)
Publisher : Institut Penelitian Dan Pengambangan Mandalika Indonesia (IP2MI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36312/jcm.v4i2.1430

Abstract

The research was conducted to create an expert advisor who can interact with the forex market automatically using the MQL4 language. Expert Advisor System is a design that consists of an array of technical analysis algorithms and indicators. The exponential moving average indicator is used in the creation of trading robots or better known as expert advisors. In designing this expert advisor, it starts with the process of making coding, implementing or designing coding, as well as testing GBP / USD and EUR / USD. In this study, the design of expert advisors that have been designed creates a system that facilitates and helps traders to trade foreign currencies. Based on the design by backtesting from January 1, 2022 to May 1, 2022 with a capital of 10000 USD, with GBP/USD pair, 4H timeframe, 1:100 laverage and EUR/USD M15 timeframe pair, leverage of 1:100 can generate a total net profit for GBP/USD of -43.81 and for a total net profit of EUR/USD of -16.35. The output of this study concluded that the use of moving averages is the basis for expert advisor logic to assist in making trading decisions even though the results have not been maximized.
Co-Authors Aang Alim Murtopo Abdullah Abdullah Adittia Agustian Afra, Alfina Fadhilah Agustia Tia Hananto Ahmad Fauzi alzahra, alika aziza Ammar Zuhair Angraeni, Rahmah Nur Ani Ani Anita Khansa Ramadanti April Lia Hananto April Lia Hananto Aprilia Putri Nardilasari Arip Solehudin Asep Haris Aulia, Aldi Aviv Yuniar Rahman Aviv Yuniar Rahman Baenil Huda Cahya Diningrat Desfianthy, Fatiya Hanifah Fatlun, Aulia Fitria Nurapriani Fitria Ratna Sari Florida Ivonia Manek Guntur, Muhamad Haerudin Haerudin Hananto, Agustia Handayani, Citra Hemant N Chaudhari Heryana, Nono Hilabi, Shofa Shofia Hilabi, Shofa Shofia Hilabi, Shofa Shofiah Hoerudin Hoerudin Huda, Baenil Intan, Melani Ismail, Dennisa Avriel Jabar Sanjaya Julia, Ina Julifer P Manurung Juniardi Karina Kurniawan, Muhamad Dicky Lutfiah, Siti Mega Rianita Kartika Mohamad Ricky Firdaus Muhammad Fahmi Miskon Muhammad Nova Naufal Zubdi Ahnaf Novalia, Elfina Nur ‘Azah Nurapriani, Fitria Nursyifa Paryono, Tukino Pradana Rizki Maulana Rini Mayasari Sabrina Amanda Salsabila Saepul Aripiyanto Satria Dawas Zhalifunas Septiana Rahayu Setiawan, Revi Shofa Shofia Hilabi Shofa Shofia Hilabi Shofa Shofiah Hilabi Shofa Shofiah Hilabi Shofia Hilabi, Shofa Sifa, Sifa Rismawati Siregar, Amril Mutoi Siti Masruroh Siti Masruroh Sofia Ramadanti Az-Zahra Sutan Faisal Syahri Susanto Tatang Rohana Teresa Abuya Titik Khawa Abdurahman Topan Setiawan Topan Trianto Tukino Tukino, Tukino tukino, tukino Tukino, Tukino Vina Andini Wahyu Aziz Ramadhani Yazid, Muhammad Abi Zahra, Nabila Zein, Selmia Aulia