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All Journal Building of Informatics, Technology and Science Jurnal Abdi Insani Jurnal Riset Sistem dan Teknologi Informasi SWAGATI: Journal of Community Service
Ade Pujianto, Ade
Magister Teknik Informatika Universitas AMIKOM Yogyakarta
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Other

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Optimisasi Algoritma Genetika dengan Particle Swarm Optimization (PSO) untuk Sistem Rekomendasi Diet Gizi bagi Penderita Diabetes Munir, Muhammad Misbahul; Pujianto, Ade; Lamuru, Haechal Aulia Muhali
Jurnal Riset Sistem dan Teknologi Informasi Vol. 1 No. 2 (2023): Jurnal Riset Sistem dan Teknologi Informasi (RESTIA) Vol. 1 No. 2
Publisher : Universitas Aisyiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30787/restia.v1i2.1289

Abstract

Diabetes, especially diabetic nephropathy, is a global health problem that is increasing in prevalence. This disease can cause various serious complications and even death. Despite the high cure rate associated with diabetes, it is important to improve the human body's immune system to reduce the risk of developing diabetes or diabetic nephropathy. One approach that can help is maintaining a diet with good nutritional coverage. This research aims to develop an artificial intelligence (AI) system that can provide recommendations for a good nutritional diet menu for diabetes sufferers. We propose the use of well-known genetic algorithms in decision making. However, to improve the accuracy and efficiency of the genetic algorithm, we will optimize it using the Particle Swarm Optimization (PSO) algorithm. The research method used is an experimental method, where we will conduct experiments to test the performance of the optimized genetic algorithm. It is hoped that the results of this research can be used as a basis for making scientific publications in accredited national journals as well as product patents for food menu recommendation systems for diabetes sufferers. The main contribution of this research is improving the performance of the genetic algorithm through the use of the PSO algorithm, which will help increase the accuracy of the nutritional diet recommendation system. In this way, it is hoped that the results of this research can provide significant benefits in efforts to prevent and manage diabetes and improve the quality of life of diabetes sufferers.
Robusta Coffee Plant Disease Identification using Dempster Shafer Method in Expert Systems Sidauruk, Acihmah; Miftakhurrokhmat, Miftakhurrokhmat; Pujianto, Ade; Salmuasih, Salmuasih
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6272

Abstract

Robusta coffee is one type of coffee that can grow well in Indonesia. Robusta coffee has 2.2% more caffeine and less sugar than Arabica coffee. This coffee may be a more interesting coffee variety from different levels of taste and thickness. In addition, Robusta coffee is very accommodating to the economy of several coffee-producing countries around the world, including Indonesia. A number of factors, especially pests and diseases, can reduce the productivity and quality of coffee plants. This is also confirmed by coffee experts who conducted research on pests and diseases in Robusta coffee plants. This study aims to develop an expert-based system that can identify problems and diseases in Robusta coffee plants using the Dempster Shafer method, and developed in a web-based platform. From the data collected from literature studies, dialogue with farmers, and consultation with an expert, 13 types of pests and diseases were obtained, and 27 symptoms of the disease. The results of this study are the development of a web-based expert system that can diagnose pests or diseases from several symptom inputs filled in by users or coffee farmers. The results of the trial of 13 test cases on the diagnosis of pests and diseases of Robusta coffee plants obtained an average accuracy value of 94%. This shows that this expert system can analyze the types of pests or diseases in Robusta coffee plants very well using the Dempster Shafer method.
Pembuatan Konten Social Media Pada PSM Kasturi Sebagai Media Informasi dan Edukasi Andriani, Ria; Pujianto, Ade
SWAGATI : Journal of Community Service Vol. 1 No. 3 (2023): November
Publisher : Universitas AMIKOM Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/swagati.2023v1i3.1296

Abstract

Bank Sampah Kasturi dirintis sejak tahun 2014 yang dibentuk secara mandiri dengan SK Nomor: 02/Kep.KD/2015 yang diputuskan oleh Kepala Desa Condongcatur, dalam perjalanannya tidak mudah untuk mengelola dan dikenal oleh masyarakat, para pengurus masih kesulitan dalam melakukan edukasi, promosi ataupun penyampian informasi mengenai bagaimana pengelolaan sampah kepada masyarakat luas agar lingkungan menjadi bersih dan mengurangi tumpukan sampah di TPS. Dari permasalahaan yang sudah dipaparkan diperlukan upaya edukasi, sosialisasi dan promosi yang lebih efektif agar dapat menjangkau masyarakat luas salah satunya dengan memanfaatkan social media. Metode pelaksanaan kegiatan dimulai dari survey, pelaksanaan pelatihan, dan evaluasi. Pelatihan ini mengajarkan pembuatan dan pengelolaan konten social media khususnya Instagram. adapun hasil yang didapatkan berupa peningkatan softskill petugas bank sampah Kasturi terkait pemanfaatan dan pengelolaan Social Media untuk melakukan edukasi, sosialisasi dan informasi tentang sampah serta mempunyai keahlian teknis dalam membuat konten konten yang bersifat edukatif selain itu juga terdapat peningkatan jumlah nasabah yang sebelumnya 180 orang menjadi 250 nasabah baru serta jumlah tabungan sampah yang sebelumnya 217,475kg setiap minggu menjadi 405,000kg disetiap minggunya.
PENDAMPINGAN DIGITALISASI UKM: PELATIHAN MEDIA SOSIAL PADA VIGAZA FARM SEBAGAI UPAYA PENINGKATAN DAYA SAING Andriani, Ria; Pujianto, Ade
Jurnal Abdi Insani Vol 11 No 4 (2024): Jurnal Abdi Insani
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/abdiinsani.v11i4.1856

Abstract

Vigaza Farm is a small and medium-sized enterprise (SME) operating in the livestock sector and was established in 2010, providing a variety of poultry and fish for consumption. Currently, the main focus of this business is on quails. Amid global and local challenges, livestock SMEs often face obstacles in accessing markets and building closer relationships with consumers, resulting in difficulties in promoting themselves and expanding their reach. This Community Service Program aims to provide assistance in the digitalization of SMEs, with an emphasis on training for the creation and management of social media platforms, particularly Instagram, for Vigaza Farm. The methods used in this activity include training, mentoring, and evaluating the use of Vigaza Farm's social media. This training successfully enhanced the branding of Vigaza Farm and improved the technical skills of its managers in utilizing technology to create engaging content. As a result, the number of followers on Instagram significantly increased from 23 to 500 followers, and sales rose from 50 bundles per week to 150 bundles shipped to various cities in Indonesia, including Jakarta. Currently, Vigaza Farm also serves as a supplier of quail eggs and quails for activities organized by the Millennial Farmers Group of Sleman. This community service program has successfully improved Vigaza Farm's digital capabilities, expanded market reach, and significantly increased sales.
Co-Authors Acihmah Sidauruk Lamuru, Haechal Aulia Muhali Miftakhurrokhmat, Miftakhurrokhmat Munir, Muhammad Misbahul Ria Andriani Salmuasih, Salmuasih