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PENYUSUNAN DATA JAJANAN KHAS PRODUKSI INDUSTRI MIKRO DI DESA GUNTURMADU DENGAN TENIK WAWANCARA TERSTRUKTUR Slamet, Slamet; Triwibowo, Deny Nugroho
ABDI MAKARTI Vol 3, No 1 (2024): ABDI MAKARTI
Publisher : Sekolah Tinggi Ilmu Ekonomi AMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52353/abdimakarti.v3i1.584

Abstract

Gunturmadu Village is a village in Mojotengah District, Wonosobo Regency, Central Java. The potential of agricultural commodities in Gunturmadu Village has not been utilized optimally because most of the agricultural products are sold directly without being processed first. There are only a few home industries on a micro scale that process agricultural products into typical snacks whose management from production to marketing is still carried out traditionally. This community service program will focus on typical dry snacks which have the potential to be developed, especially for packaging and online marketing. The author carried out a community service program by compiling data on typical snacks produced by the micro industry in Gunturmadu Village using structured interview techniques. The interview data was then processed and edited into a catalog book of typical snacks from Gunturmadu Village, resulting in 20 typical dry snacks. It is hoped that the catalog book for typical dry snacks produced by micro industries in Gunturmadu Village in 2023 will become literature and can ignite the enthusiasm of micro industry players and the younger generation to develop micro industries in Gunturmadu Village.
Klasifikasi Status Gizi Balita Menggunakan Algoritma Support Vector Machine dengan Optimasi Grid Search Cross-Validation Nadroh, Azkiyatun; Triwibowo, Deny Nugroho; Sumantri, R. Bagus Bambang
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 8 No. 2 (2024): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol8No2.pp250-257

Abstract

Toddlers are children aged 0 to 59 months who experience rapid growth and development and require a higher intake of nutrients. This study aims to classify the nutritional status of toddlers using the Support Vector Machine (SVM) algorithm with Grid Search optimization. The quality of a toddler's nutrition significantly affects their growth and development, and malnutrition is a major issue in Indonesia. Data were obtained from Posyandu Desa Jagalempeni, comprising a total of 512 toddler data entries. After undergoing pre-processing and feature engineering, the data were classified using SVM. The initial results showed an accuracy of 80%. Following the application of Grid Search optimization with the Radial Basis Function (RBF) kernel, accuracy increased to 86.17%. These results indicate that Grid Search is effective in optimizing SVM model parameters and improving classification performance.
Klasifikasi Jenis Sampah Berbasis Convolutional Neural Network dengan Optimasi Hyperparameter Tuning Arsitektur Mobilenet Kuncoro, Dimas Febri; Wirasto, Anggit; Triwibowo, Deny Nugroho
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 9 No. 1 (2025): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol9No1.pp130-144

Abstract

Waste management in Indonesia faces significant challenges with an increasing volume reaching approximately 175,000 tons per day. Public awareness of the dangers associated with improper waste disposal remains low, as many continue to litter indiscriminately. Waste sorting is the most effective method, involving separation based on waste types. Manual waste sorting is nonetheless inefficient, as it requires large spaces, substantial labor, and is prone to errors. This study aims to develop a waste classification model based on Convolutional Neural Network (CNN) with hyperparameter tuning optimization for the MobileNet architecture. The research adopts the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology and utilizes datasets from three waste categories organic, inorganic, and hazardous and toxic materials (B3) sourced from open Kaggle datasets. Model training was conducted using the MobileNet architecture with hyperparameter tuning optimization and resulting in optimal parameters Adam optimizer, learning rate of 0.01, batch size of 32, and 256 neurons. The results show that the model achieved 96% accuracy before optimization which increased by 2% to 98% after optimization. The model demonstrated high computational efficiency with the number of floating-point operations per second reaching 1.146 GFLOPS.
Analisis Pengalaman Pengguna terhadap Platform Konferensi Video untuk Mendukung Perkuliahan: Studi Evaluasi Menggunakan UEQ+ Setia Sandi Ariyanto, Arif; Triwibowo, Deny Nugroho; Trivilia, Indah
Jurnal Sistem Informasi, Manajemen dan Teknologi Informasi Vol. 3 No. 2 (2025): Juli
Publisher : STMIK Palangkaraya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33020/jsimtek.v3i2.943

Abstract

Penelitian ini bertujuan untuk menganalisis pengalaman pengguna (User Experience/UX) terhadap platform konferensi video (Google Meet, Zoom Meeting, dan Microsoft Teams) dalam mendukung aktivitas perkuliahan. Studi evaluasi ini menggunakan metode User Experience Questionnaire Plus (UEQ+) yang mencakup enam dimensi utama, yaitu: Attractiveness, Efficiency, Intuitive Use, Visual Aesthetic, Quality of Content, dan Trust. Data dikumpulkan dari 80 partisipan yang merupakan mahasiswa pengguna berbagai platform konferensi video yang umum digunakan dalam konteks perkuliahan. Hasil penelitian menunjukkan respons positif terhadap ketiga aplikasi tersebut, yang ditunjukkan oleh nilai rata-rata keseluruhan pada setiap skala yang melebihi 0,8. Selain itu, variasi data pada seluruh skala juga tercatat berada pada tingkat yang sangat baik dan dapat diterima. Hasil pengukuran menunjukkan reliabilitas yang tinggi di semua skala. Aplikasi Zoom Meeting dinilai lebih unggul dibandingkan Google Meet dan Microsoft Teams dalam seluruh kriteria, kecuali pada dimensi Trust yang didominasi oleh Google Meet.