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All Journal Techno.Com: Jurnal Teknologi Informasi Journal of Innovation and Applied Technology Jurnal Teknologi Informasi dan Ilmu Komputer Journal of Information Technology and Computer Science Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat Prosiding Seminar Nasional Pengabdian Kepada Masyarakat
Dany Primanita Kartikasari
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Other

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Community Empowerment Strategy of Pondok Agung, Kasembon Sub-district through Biogas Technology to Reduce Cattle Waste Pollution in Water Maimunah, Yunita; Kilawati, Yuni; Muttaqin, Adharul; Amrillah, Attabik Mukhammad; Kartikasari, Dany Primanita
PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat Vol. 10 No. 8 (2025): PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat
Publisher : Institute for Research and Community Services Universitas Muhammadiyah Palangkaraya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33084/pengabdianmu.v10i8.9544

Abstract

The community-based biogas project in Kasembon sub-district focuses on utilising livestock manure as a renewable energy source and organic fertiliser producer to improve community welfare and reduce negative environmental impacts. Through an Asset-Based Community Development (ABCD) approach, the project identifies local assets, such as land and manure, and empowers the local community to maintain the biogas system. The biogas production process produces environmentally friendly energy and reduces dependence on LPG. In addition, the organic fertiliser made from biogas waste can be utilised in agriculture to improve productivity and soil health. The implementation of ABCD encourages collaboration between the community, government, and university, and creates new economic opportunities by selling organic fertiliser. However, challenges such as dependence on external assistance, high initial capital requirements, and technical limitations hinder long-term sustainability.
Automated Cloud Migration System for Permissioned Blockchain Infrastructure Annisa, Faradiba; Bhawiyuga, Adhitya; Akbar, Sabriansyah Rizqika; Shaffan, Nur Hazbiy; Kartikasari, Dany Primanita
Journal of Information Technology and Computer Science Vol. 10 No. 2: August 2025
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.2025102602

Abstract

Blockchain is a technology that stores data in a distributed manner. There are two types of blockchains: permissionless and permissioned. In permissionless blockchains, nodes are operated by anonymous participants who do not know each other. Meanwhile, permissioned blockchain nodes run on a private infrastructure owned by the organizations participating in the blockchain network. This infrastructure may need to be migrated for various reasons, either from on-premises to the cloud or between clouds. Therefore, in this research, a migration system for permissioned blockchain infrastructure is developed. This migration system operates automatically to reduce human errors, inconsistencies, and time inefficiencies. To achieve automation, Infrastructure as Code (IaC) and automation tools are used. The IaC tool is used to automate infrastructure provisioning on the target cloud platform, while the automation tool is used to configure and deploy the blockchain on virtual machines in the target cloud. The chosen cloud platform is a public cloud. The experiment on the automated migration system focuses on two aspects. The first aspect evaluates the system's capability to perform infrastructure provisioning, blockchain configuration, and blockchain deployment on the target cloud platform. The second aspect assesses the migrated blockchain's functionality compared to the source infrastructure. The experimental results demonstrate that the automated migration system can successfully provision infrastructure, configure, and deploy the blockchain on virtual machines in the target cloud. Furthermore, the results confirm that the blockchain on the target infrastructure can add new data and access previously generated data within the blockchain.
Prediksi Cuaca Pada Data Time Series Menggunakan Adaptive Neuro Fuzzy Inference System (ANFIS) Dewi, Candra; Kartikasari, Dany Primanita; Mursityo, Yusi Tyroni
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 1 No 1: April 2014
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (669.63 KB) | DOI: 10.25126/jtiik.201411100

Abstract

AbstrakInformasi mengenai kondisi atmosfer yang cepat,akurat, dan terperinci sangat diperlukan oleh berbagai sektor. Salah satumetode yang dapat digunakan untuk melakukan prediksi dan peramalan model yangkompleks dengan akurasi yang tinggi adalah Adaptive Neuro Fuzzy InferenceSystem (ANFIS). Dengankemampuan metode ini untuk melakukan prediksi dan peramalan, pada penelitianini dilakukan perbandingan kinerja dari kedua kemampuan ANFIS tersebut padadata time series cuaca berdasarkan parameter-parameter atmosfir yangmempengaruhinya.Padapenelitian ini, metode ANFIS baik untuk proses prediksi maupun peramalan diimplementasidengan struktur standar ANFIS yaitu lima layer. Namun pada proses peramalan dilakukan penggabungan dengan metode moving average untuk meramalkan nilai parameter input pada saat pengujian. Pengujian dilakukanpada data latih 40%, 50% dan 60% dari total data. Selain itu, pengujian jugadilakukan dengan mengelompokkan data berdasarkan musim, yaitu kemarau danpenghujan.Hasil ujicoba menunjukkan bahwa metode ANFIS cukup baik diterapkan untuk proses prediksijika tanpa pengelompokan data berdasarkan musim. Namun jika dilakukanpengelompokan berdasarkan musim, kemampuan ANFIS dalam melakukan peramalanmemiliki tingkat akurasi yang lebih tinggi dengan nilai error yang cukup rendah.Kata kunci: prediksi cuaca,peramalan cuaca, data time series, ANFISAbstractRapid and accurate information on the atmospheric conditions is required by the various sectors. One ofthe methods can be used to perform prediction and forecasting of complex modelwith high accuracy is AdaptiveNeuro Fuzzy Inference System(ANFIS).According to these two capabilitiesof ANFIS, this research is aimed to conduct comparison of accuracy on weathertime series data. This research implemented ANFIS using standard ANFISarchitecture that consists of five layers both to predict and to forecast theweather. However, the forecasting process combined ANFIS and moving averagemethod to forecast the input parameters were used at testing. This researchperformed learning process using 40%, 50% and 60% of total data. Beside, thelearning process also has been done on data was grouped into two groups basedon the season. The testing result showed ANFIS has better performance forprediction the data that were not grouped based on the season. However, ANFIShas better accuracy and lower error since the learning and testing were done onthe data that was grouped based on season.Keywords: weather prediction, weather forecasting,time series data, ANFIS
Optimasi Penjadwalan Mata Kuliah Menggunakan Metode Algoritma Genetika dengan Teknik Tournament Selection Sari, Yuslena; Alkaff, Muhammad; Wijaya, Eka Setya; Soraya, Syarifah; Kartikasari, Dany Primanita
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 6 No 1: Februari 2019
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (3621.855 KB) | DOI: 10.25126/jtiik.2019611262

Abstract

AbstrakBagi sebuah perguruan tinggi, penjadwalan perkuliahan merupakan suatu kegiatan yang sangat penting   untuk   dapat   terlaksananya   proses belajar mengajar   yang   baik.  Dimana   dalam   proses  belajar mengajar dapat dilakukan oleh semua pihak yang terkait, bukan hanya bagi dosen yang mengajar, tetapi juga bagi mahasiswa yang mengambil mata kuliah. Dalam penyusunan jadwal, ada beberapa variabel yang mempengaruhi yaitu: ruangan yang tersedia, jumlah mata kuliah yang diselenggarakan, waktu yang ada dan ketersediaan dosen yang mengajar. Oleh karena itu tujuan dari penelitian ini adalah merancang suatu sistem yang dapat membuat atau menyusun   jadwal    perkulihaan    secara  teroptimasi. Metode dalam proses pembuatan jadwal perkuliahan secara otomatis pada penelitian ini menggunakan metode algoritma genetika dengan teknik seleksi turnamen. hasil pengujian sistem dapat memberikan kemudahan dan kecepatan kepada user atau Program Studi Teknologi Informasi dalam proses pembuatan atau penyusunan jadwal untuk    perkuliahan,    yaitu hanya diperlukan waktu sekitar 14,7 menit dibandingkan dengan proses manual yang memerlukan waktu sekitar 2 (dua) hari.AbstractFor a college, the university course timetabling is is an activity that’s very important for the implementation of good teaching and learning process. In  teaching  and  learning  process  can be done    by    all    related    parties,   not    only    for Lecturers who teach, but also for students who take the course. In the preparation of the schedule, there are several variables that affect the: the available space, the number of courses held, the time available and the availability of lecturers  who  teach. Therefore, the  purpose  of this research is to design a system that can create or arrange optimization schedule optimally. Methods in the process of making university course   timetabling   automatically   in   this study using genetic algorithm method with tournament selection.
Implementasi Arsitektur Web Server Cluster Menggunakan Single Board Computer untuk Menunjang Kebutuhan High Availability System Setiawan, Roisul; Kartikasari, Dany Primanita; Rahayudi, Bayu
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 8 No 2: April 2021
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2021824512

Abstract

Untuk mewujudkan ketahanan pangan, diperlukan mekanisme pengumpulan data secara real-time dari produsen  bahan pangan, pendisitribusi bahan pangan sampai pengolah bahan pangan. Namun tidak semua organisasi yang berkecimpung dalam distribusi pangan memiliki infrastruktur sistem informasi yang cukup baik. Untuk mengatasi kendala infrastruktur, penelitian ini mengusulkan untuk membangun arsitektur web server cluster yang dapat menunjang kebutuhan high availability system menggunakan single board computer.  Komponen arsitektur terdiri dari dua tier yaitu: frontend dan backend. Untuk menjamin kehandalan sistem, arsitektur  yang diusulkan didukung dengan komponen load balancing, mekanisme failover dan replikasi database. Sistem telah diuji berasarkan kebutuhan fungsional dan kebutuhan non-fungsional yang sudah didefinisikan sesuai kebutuhan organisasi. Dari hasil pengujian, tingkat availabilitas yang dihasilkan sebesar 95.83%. AbstractTo achieve food security, a real-time data collection mechanism is needed from food producers, food distribution to food processing. However, not all organizations involved in food distribution have adequate information system infrastructure. To overcome infrastructure constraints, this study proposes to build a web server cluster architecture that can support the needs of a high availability system using a single board computer. The architectural component consists of two tiers, namely: frontend and backend. To ensure system reliability, the proposed architecture is supported by load balancing components, failover mechanisms, and database replication. The system has been tested based on functional requirements and non-functional requirements that have been defined according to organizational requirements. From the test results, the resulting availability level is 95.83%.
Household-Scale Biodigester Application: Transforming Livestock Waste into a New Source of Income in Kasembon Subdistrict Kilawati, Yuni; Amrillah, Attabik Mukhammad; Maimunah, Yunita; Kartikasari, Dany Primanita; Muttaqin, Adharul
Journal of Innovation and Applied Technology Vol 12, No 1 (2026)
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jiat.2025.012.01.10

Abstract

Livestock farmers in Kasembon Subdistrict face dual challenges: severe environmental pollution from dumping manure directly into rivers, and economic strain due to high dependence on costly LPG and synthetic fertilizers. This community service program introduces household-scale biodigesters to solve both environmental and economic issues simultaneously. Involving 20 local participants through the Participatory Rural Appraisal (PRA) method, capacity building, and technical assistance, the intervention yielded highly significant outcomes. LPG consumption plummeted from 3.8 to 0.4 cylinders monthly. Behavior completely transformed: previously, 80% of farmers polluted water bodies, whereas today 100% utilize their waste as biodigester feedstock, effectively halting river pollution. Economically, this circular system generates average monthly savings of Rp 206,000 per household by substituting LPG and chemical fertilizers. Participants' technical skills also surged from 1.95 to 4.61. Ultimately, biodigester technology successfully mitigates environmental damage while empowering rural communities toward sustainable energy independence and food security
Pengembangan Model Horizontal Autoscaling pada Kubernetes Menggunakan Reinforcement Learning Berbasis Fuzzy logic Brata, Gede Indra Adi; Yahya, Widhi; Kartikasari, Dany Primanita
Jurnal Pengembangan Teknologi dan Ilmu Komputer Vol 10 No 5 (2026): Mei 2026
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Horizontal Pod Autoscaling (HPA) merupakan mekanisme penting dalam kubernetes untuk menjaga keseimbangan antara kualitas layanan dan penggunaan sumber daya. HPA yang umum digunakan masih berbasis ambang batas statis (threshold based), sehingga kurang adaptif terhadap perubahan beban kerja yang dinamis dan berpotensi menyebabkan over provisioning atau under provisioning. Penelitian ini mengusulkan pendekatan autoscaling berbasis reinforcement learning (RL) menggunakan algoritma Q-learning dan Q-learning berbasis fuzzy logic untuk mengatasi keterbatasan tersebut. Penerapan Q-learning secara langsung pada lingkungan dengan variabel observasi kontinu menghadapi permasalahan curse of dimensionality, di mana ruang state tumbuh secara eksponensial seiring bertambahnya variabel observasi sehingga menyulitkan konvergensi kebijakan optimal. Integrasi fuzzy logic bertujuan mengatasi permasalahan tersebut melalui reduksi ruang state menggunakan diskretisasi berbasis kategori linguistik. Eksperimen dilakukan dengan membandingkan tiga metode autoscaling, yaitu Q-learning, Q-learning berbasis fuzzy logic, dan HPA, menggunakan metrik utama tingkat pelanggaran SLO dan rata-rata jumlah replika. Hasil pelatihan selama 60 episode menunjukkan bahwa Q-learning berbasis fuzzy logic menghasilkan cumulative reward sebesar -453,4286, jauh lebih tinggi dibandingkan Q-learning sebesar -3.504,2705. Jumlah state yang terbentuk pada Q-learning mencapai 1.855, sedangkan pada Q-learning berbasis fuzzy logic hanya 21 state, menunjukkan reduksi ruang state sekitar 88 kali lebih kecil. Pada tahap pengujian, Q-learning menghasilkan tingkat pelanggaran SLO sebesar 70,9%, sedangkan Q-learning berbasis fuzzy logic hanya 13,8%, dan HPA sebesar 0,2%. Nilai P90 dan P95 pada Q-learning melampaui ambang batas SLO 1000 ms, sementara Q-learning berbasis fuzzy logic dan HPA tetap berada di bawah ambang batas tersebut. Dari sisi efisiensi sumber daya, rata-rata replika yang digunakan Q-learning berbasis fuzzy logic adalah 5,28, lebih rendah dibandingkan HPA sebesar 8,45 replika. Hasil penelitian menunjukkan bahwa integrasi fuzzy logic mampu mengatasi permasalahan curse of dimensionality pada Q-learning, sekaligus memberikan keseimbangan antara pemenuhan SLO dan efisiensi penggunaan sumber daya dalam mekanisme autoscaling Kubernetes
Co-Authors Aaron, Anthony Adharul Muttaqin Adhitya Bhawiyuga, Adhitya Anggara, Krisna Dwi Annisa, Faradiba Astutik, Krestiana Yuli Attabik Mukhammad Amrillah Bakhtiar, Fariz Andri Bayu Rahayudi Brata, Gede Indra Adi Candra Dewi Eko Sakti Pramukantoro, Eko Sakti Imaduddin, Muhammad Fakhri Masyfa, Faiz Hilmawan Muhammad Alkaff Pangestu, Muhammad Faisal Reza Andria Siregar Sabriansyah Rizqika Akbar Setiawan, Roisul Shaffan, Nur Hazbiy Syarifah Soraya Tibyani, - Wicaksono, Bagoes Sandy Widhi Yahya Wijaya, Eka Setya Yudhanto, Alif Alamsyah Yuni Kilawati Yunita Maimunah Yusi Tyroni Mursityo Yuslena Sari, Yuslena