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All Journal JIKO (Jurnal Informatika dan Komputer) International Journal of Social Science Journal of Intelligent Software Systems Jurnal Teknologi Informasi
Danny Kriestanto
Universitas Teknologi Digital Indonesia
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Education Environmental Science Health Professions Languange, Linguistic, Communication & Media Medicine & Pharmacology Public Health Social Sciences Other

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BINA SEJAHTERA EMPLOYEE COOPERATIVE FINANCIAL INFORMATION SYSTEM BASED ON SAK-ETAP FOR MANAGERIAL DECISIONS Heru Agus Triyanto; Danny Kriestanto; Sur Yanti
International Journal of Social Science Vol. 2 No. 4: December 2022
Publisher : Bajang Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53625/ijss.v2i4.4262

Abstract

The Bina Sejahtera employee cooperative or abbreviated as Kopkar Binatara STMIK AKAKOM has a savings and loan business unit and is also a shop division. The system was designed by creating context diagrams, level 0 DAD, relationships between tables and data dictionaries, input designs, and main menus and system views. Bina Sejahtera Employee Cooperative Financial Information System Based on SAK-ETAP Managerial Decisions only involve 2 (two) external entities, namely members and management of cooperatives. One of the outputs of this system will be the SHU report. Cooperative administrators provide data on cooperative management, position data, type of deposit data, savings transaction data, loan type data, loan transaction data, installment data, retrieval data, SHU data and obtain member reports, job reports, cooperative management reports, periodic savings reports, savings report per member, loan report per member, loan report per period, retrieval report per member, retrieval report per period, bad credit report per period, ceiling report per member, overall ceiling report, installment report per period, installment report per member, report on fines per period, interest report per period, SHU report per member, overall SHU report, Profit / Loss Report, Change in Capital Report, Balance Sheet Report from Bina Sejahtera Employee Cooperative Financial Information System Based on SAK-ETAP. The reports obtained from the system are complex but useful for managerial decisions
RANCANGAN DAN IMPLEMENTASI APLIKASI MENTORING MENGGUNAKAN FRAMEWORK LARAVEL Arrio Saputra; Muhammad Agung Nugroho; Femi Dwi Astuti; Danny Kriestanto
JuTI "Jurnal Teknologi Informasi" Vol 1, No 2 (2023): Februari 2023
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat - Universitas Teknologi Digital Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (631.116 KB) | DOI: 10.26798/juti.v1i2.811

Abstract

Program Magang dan Studi Independen Bersertifikat (MSIB) di Yayasan Hasnur Centre mengharuskan mentor menulis laporan kegiatan mentoring, Pada platform website kampus merdeka mitra/PIC/HR hanya dapat melihat laporan harian maupun mingguan dari para mahasiswa magang dan laporan mentor tidak dapat dilihat oleh mitra/PIC/HR sebagai pemegang akun mitra. Sehingga pada penelitian ini membuat website mentoring platform yang mana website ini akan digunakan untuk mencatat atau menuliskan kegiatan mentoring Yayasan Hasnur Centre  Quality Internship Program (YHC QuIP) kampus merdeka yang dilakukan oleh mentor setiap hari kerja serta melampirkan dokumentasinya. Mentoring platform ini mentor dapat menuliskan kegiatan mentoring yang hanya bisa dibuat satu kali sehari akan tetapi jika mentor lupa menuliskan kegiatan mentoring pada hari itu maka akan dibuat otomatis oleh sistem, akan tetapi kegiatan mentoring hanya dapat diedit atau diubah selama dua hari sebelumnya dan kegiatan mentoring hanya dapat dihapus pada hari itu juga. Aplikasi Mentoring platform mempermudah mentor dalam menulis aktivitas/laporan/jurnal serta mempermudah HR/mitra sebagai admin untuk melihat seluruh laporan para mentor karena data yang disimpan sudah terpusat  
Polynomial Regression Method and Support Vector Machine Method for Predicting Disease Covid-19 in Indonesia Bambang Purnomosidi Dwi Putranto; Moh. Abdul Kholik; Muhammad Agung Nugroho; Danny Kriestanto
Journal of Intelligent Software Systems Vol 2, No 1 (2023): July
Publisher : LPPM UTDI (d.h STMIK AKAKOM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiss.v2i1.931

Abstract

The COVID-19 pandemic has become a major threat to the entire country. According to the WHO report, COVID-19 is a severe acute respiratory syndrome transmitted through respiratory droplets resulting from direct contact with patients. This study of data history is then processed using data mining prediction methods, namely the Polynomial Regression method compared to the Support Vector Machine method. Of the two methods will be sought the most accurate method by testing accuracy with MAE, MSE, and also MAPE to get the results of covid-19 predictions in Indonesia. Based on the comparison of test results through various scenarios against both methods, the Polynomial Regression method obtained the smallest test value, resulting in an accuracy value of MAE = 4146.025749867596, MSE = 19031800.02642069, MAPE = 0.006174164877416524. Polynomial regression is the best-recommended method
Analisis Perbandingan GraphQL dan REST API pada Aplikasi Menu Restoran dengan Node.js Agung Prasetyo; Danny Kriestanto
JURNAL INFORMATIKA DAN KOMPUTER Vol 9, No 2 (2025): Juni 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat - Universitas Teknologi Digital Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiko.v9i2.1521

Abstract

Penelitian ini bertujuan untuk menganalisis perbandingan performa antara GraphQL dan REST API pada aplikasi menu restoran berbasis Node.js, dengan fokus pada aspek waktu respons, penggunaan bandwidth, dan fleksibilitas. Masalah yang diangkat adalah menentukan solusi API yang optimal untuk aplikasi yang membutuhkan pengelolaan data secara efisien dan cepat. Pengujian dilakukan di lingkungan cloud menggunakan layanan gratis untuk menggambarkan kondisi nyata. Pendekatan penelitian dilakukan dengan pengujian performa menggunakan K6, alat yang digunakan untuk mensimulasikan beban permintaan pada server. Parameter yang diukur meliputi jumlah total permintaan, rata-rata waktu respons, volume data yang diterima dan dikirim, serta stabilitas server di bawah beban tinggi. Hasil analisis menunjukkan bahwa waktu respons GraphQL dan REST API tidak berbeda secara signifikan. Namun, GraphQL memiliki keunggulan dalam efisiensi bandwidth, karena hanya mengirim data yang diminta oleh klien, sedangkan REST API cenderung kurang fleksibel dan menghasilkan pengiriman data berlebih yang tidak selalu diperlukan klien. Hasil penelitian ini menunjukkan bahwa GraphQL unggul dibandingkan REST API dalam hal efisiensi data, kestabilan performa, dan fleksibilitas pengambilan data. GraphQL lebih hemat bandwidth dan memberikan kontrol lebih besar kepada klien dalam memilih data yang dibutuhkan, menjadikannya pilihan terbaik untuk aplikasi dengan kebutuhan data dinamis dan skalabilitas tinggi. Namun, REST API tetap efektif untuk aplikasi dengan arsitektur sederhana yang tidak memerlukan kustomisasi data kompleks.
PERFORMANCE ANALYSIS OF LOGISTIC REGRESSION ALGORITHM IN OPINION SEGMENTATION OF INDOSAT NETWORK SERVICE REVIEWS Sandy Ananda Dwi; Danny Kriestanto; Ajie Al Qadri Anwar; Syahrur Ro'uf; Lintang Suci Rochmana; Muhammad Agung Nugroho
Journal of Intelligent Software Systems Vol 4, No 1 (2025): Juli 2025
Publisher : LPPM UTDI (d.h STMIK AKAKOM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiss.v4i1.2004

Abstract

In the era of the industrial revolution 4.0, where the use of network services has become a basic need and cannot be separated from daily activities, the massive number of network service users can be proven by the increasing number of people using digital platforms to search for information, express opinions or even just to communicate with each other, currently network services are available in the form of digital platforms that can be used to purchase network data packages or just to monitor the quality of network services, therefore this study aims to analyze user sentiment towards network services that have been launched by the Indosat provider based on the results of user reviews sourced from the digital platform using a machine learning approach and a logistic regression algorithm model to determine the segmentation of opinions that are widely expressed on the digital platform. The results of this study indicate that the logistic regression algorithm is able to analyze patterns of consumer characteristics with good accuracy in the algorithm model, and the results of the accuracy of the algorithm model in finding segmentation patterns in sentiment opinions reach an accuracy value of 85%, precision 81%, recall 77% and f1-score 79% to predict an opinion that has negative and positive sentiment during testing, then network speed, connection disruption and network data package prices are one of the factors that can influence an opinion regarding negative and positive sentiment.
CLASSIFICATION OF OIL PALM FRUIT CROSS-SECTIONS USING HSV FEATURE EXTRACTION AND GAUSSIAN NAÏVE BAYES Teguh Junian Kuswanto; WIDYASTUTI ANDRIYANI; Rikie Kartadie; Bambang Purnomosidi D.P; Danny Kriestanto
Journal of Intelligent Software Systems Vol 4, No 2 (2025): Desember 2025
Publisher : LPPM UTDI (d.h STMIK AKAKOM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiss.v4i2.2310

Abstract

Accurate identification of oil palm fruit varieties is essential for supporting breeding programs and optimizing seed quality in plantation operations. Manual approaches often lead to inconsistencies due to the high visual similarity among fruit types, particularly between dura and tenera. This study proposes an automatic classification model for oil palm fruit cross-sections using HSV-based color feature extraction combined with a Gaussian Naïve Bayes classifier. A dataset of 186 cross-sectional fruit images was used, consisting of 90 training samples and 96 testing samples representing the dura, pisifera, and tenera varieties. The methodology includes preprocessing, segmentation, HSV feature extraction, model training, and performance evaluation through a confusion matrix. Experimental results show that the proposed model achieves an accuracy of 85%, with misclassifications primarily occurring in the tenera class due to its close resemblance to the dura variety. Compared to Linear Discriminant Analysis (LDA), the proposed approach demonstrates faster computation time and competitive accuracy. These findings indicate that Gaussian Naïve Bayes, supported by HSV feature descriptors, provides an efficient solution for lightweight and cost-effective digital classification of oil palm fruit varieties
Co-Authors Agung Prasetyo Ajie Al Qadri Anwar Andriyani, Widyastuti Arrio Saputra Bambang Purnomosidi Dwi Putranto Femi Dwi Astuti Heru Agus Triyanto Lintang Suci Rochmana Moh. Abdul Kholik Muhammad Agung Nugroho Nugroho, Muhammad Agung Rikie Kartadie Sandy Ananda Dwi Sur Yanti Syahrur Ro'uf Teguh Junian Kuswanto