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IMPLEMENTASI ARTIFICIAL NEURAL NETWORK PADA FIELD PROGRAMMABLE GATE ARRAY (FPGA) DALAM SISTEM IDENTIFIKASI ODOR Sari, Dini Fakta; Rivai, Muhammad; Mujiono, Totok
Jurnal Teknologi Informasi RESPATI Vol 10, No 28 (2015)
Publisher : Universitas Respati Yogyakarta

ABSTRAKPenggunaan Field Programmable Gate Array (FPGA) untuk implementasi artificial neural network memberikan fleksibilitas dalam sistem pemrograman. Implementasi digital pada artificial neural network menggunakan FPGA dan menggunakan fungsi aktivasi nonlinier. VHDL digunakan untuk mengimplementasikan artificial neural network pada FPGA Xilinx XC3S500E-FG320 dengan perangkat lunak Xilinx ISE Webpack 8.2i. Kecepatan operasi FPGA Xilinx XC3S500E-FG320 dapat ditingkatkan dengan menggunakan metode lookup table (LUT). Jumlah LUT yang digunakan untuk perancangan artificial neural network dengan 3 neuron pada lapisan input, 4 neuron pada lapisan output dengan 1 neuron pada lapisan tersembunyi adalah sebesar 1407 LUT, untuk 5 neuron pada lapisan tersembunyi sebesar 4549 LUT, untuk 10 neuron pada lapisan tersembunyi sebesar 6378 LUT dan untuk 15 neuron pada lapisan tersembunyi sebesar 10084 LUT. Sistem dentifikasi odor, dilengkapi dengan sensor resonator kuarsa, pengkondisi sinyal, FPGA dan display. Model Multi Layer Perceptron (MLP) dengan metode pembelajaran Back Propagation (BP) yang digunakan untuk klasifikasi odor. Artificial neural network terdiri dari 3 neuron pada lapisan input, 10 neuron pada lapisan tersembunyi dan 4 neuron pada lapisan output yang diimplementasikan pada FPGA. Tingkat keberhasilan artificial neural network untuk identifikasi amoniak sebesar 93%, untuk pertamax sebesar 90%, untuk alkohol sebesar 92% dan untuk minyak tanah sebesar 85%.Kata kunci : Odor, sistem identifikasi odor, Artificial neural network, dan FPGA.

CLUSTERING ASPEK KOGNITIF MAHASISWA TERHADAP PEMANFAATAN TEKNOLOGI INFORMASI Kriestanto, Danny; Sari, Dini Fakta
Jurnal Teknologi Informasi RESPATI Vol 11, No 31 (2016)
Publisher : Universitas Respati Yogyakarta

ABSTRAKKemampuan memahami sebuah aplikasi merupakan hal yang wajib dimiliki, khususnya bagi mahasiswa strata satu. Penggunaan teknologi informasi dan aspek kognitif mahasiswa dinilai memiliki pengaruh besar terhadap kemampuan seseorang dalam menyerap informasi baru dari lingkungannya sehingga mencapai tingkat kreativitas yang tinggi.Penelitian ini akan menggali kemampuan mahasiswa strata satu, dalam kasus ini adalah mahasiswa jenjang strata satu pada salah satu perguruan tinggi di Yogyakarta, untuk mengetahui tingkat kemampuan mahasiswa tersebut di dalam enam tahapan aspek kognitif yang dicetuskan oleh Bloom. Walaupun tujuan penelitian ini adalah untuk menemukan kluster dengan menggunakan metode K-Means, namun akan juga digunakan metode pohon keputusan sebagai bahan perbandingan.Hasil dari penelitian ini menemukan bahwa terdapat gap yang cukup besar di para mahasiswa dalam hal aspek kognitif.Kata Kunci : Aspek kognitif, data mining, K-Means, Bloom, teknologi informasi, pohon keputusan

Dempster Shafer Analysis in Mental and Emotional Health Monitoring Nasyuha, Asyahri Hadi; Sari, Dini Fakta; Azanuddin, Azanuddin; Khoiri, Muhammad Hafidz Ady
Paradigma - Jurnal Komputer dan Informatika Vol. 26 No. 2 (2024): September 2024 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v26i2.5044

Monitoring and diagnosing mental and emotional health is a significant challenge in the healthcare field due to its complex and subjective nature. This research aims to develop an expert system using the Dempster-Shafer method in monitoring and diagnosing mental and emotional health conditions. The Dempster-Shafer method was chosen because of its ability to handle uncertainty and combine various evidence from different information sources. This analysis is designed to identify seven types of mental and emotional illness by considering twenty-four related symptoms. The results of the analysis show that this expert system can provide a more accurate and comprehensive assessment compared to conventional methods. It is hoped that the implementation of this expert system can be an effective tool for medical personnel in making diagnoses and determining appropriate treatment steps for patients with mental and emotional health conditions. This study also highlights the potential of the Dempster-Shafer method in other applications that require evidence-based analysis under uncertainty.

Aplikasi Pelacakan Alumni STMIK AKAKOM Berbasis Sistem Informasi Geografis Iskandar, Edi; Sari, Dini Fakta
Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Vol 9 No 1 (2019): Maret 2019
Publisher : STMIK Palangkaraya

Alumni is a product of an educational institution. The quality of the alumni shows the quality of the educational institution. The fact is increasingly felt, especially for college alumni. This is because alumni of college will directly come into contact with the world of work. Tracer study activity is one of the activities that have a very strategic value in the development of a college. STMIK Akakom is one of the universities in the city of Yogyakarta is required to always mempebaiki quality of education process accompanied by efforts to increase its relevance in the framework of global competition. In addition Tracer study is one effort that is expected to provide information to evaluate educational outcomes in STMIK Akakom. This information is used for further development in ensuring educational quality. This research produces alumni application of STMIK Akakom alumni by utilizing Geographic information system to map the location where alumni work, besides that it also displays alumni data in the form of year of admission, graduation year, long waiting time to work first after graduation

BUSINESS INTELLIGENCE FOR DETERMINING PROMOTIONAL MEDIA Alvianingrum, Ante Wahyu; Sari, Dini Fakta; Syadziliy, Abil Hasan Ali Asy; Supriatin, Supriatin; Nur’aini, Nur’aini
Journal of Intelligent Software Systems Vol 3, No 2 (2024): December 2024
Publisher : LPPM UTDI (d.h STMIK AKAKOM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiss.v3i2.1505

Abstract—In 2021-2022, SMK Dirgantara Putra Bangsa experienced a decrease in the number of prospective students who registered. This condition encourages schools to innovate and be creative in choosing effective promotional media in disseminating information on New Student Admissions (PPDB) to the public widely and routinely, with the aim of attracting more new students. This promotional effort involves the use of various types of promotional media. Business intelligence plays a role in processing organizational data into useful information to improve performance, by analyzing historical data which is then used to support decision making and planning. The K-Means algorithm is one of the clustering methods that is often used because of its ease of implementation and its ability to minimize the sum of squared error (SSE) value between data and the specified centroid. The collaboration between Business Intelligence and K-Means clustering is expected to help SMK Dirgantara Putra Bangsa in choosing the right promotional media and creating new innovations in disseminating PPDB information to the public.Keywords— PPDB, Business intelligence, K-Means clustering, Media Promosi

SALES PREDICTION OF VEGETABLE SEED PRODUCTS USING SIMPLE LINEAR REGRESSION Sari, Dini Fakta; Sofian, Muhammad Ali; Nurcahyo, Agung Wilis; Wiharyanto, Kelik; Pereira, Elisabet da Conceição
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.v4i1.2001

The growth of the modern agricultural sector drives the need for an accurate sales prediction system, especially for vegetable seed products that are highly dependent on the season and market demand. An imbalance between stock and demand can cause losses, either in the form of overstock or undersupply. This condition requires a data-based planning strategy to ensure stock availability according to actual needs in the field. A historical data-based sales prediction approach is a relevant solution to optimize the distribution and procurement process. This study aims to apply a simple linear regression method in predicting vegetable seed sales based on historical data for one year. The prediction model is built using the time variable (month) as the independent variable and the number of seed requests as the dependent variable. This technique was chosen because of its ability to identify linear relationship patterns between time and sales trends in a simple but effective way. The data used comes from internal records of farmers and distributors, which are then classified into two main categories: leafy vegetable seeds (spinach, kale, mustard greens) and fruit vegetable seeds (tomatoes, chilies, eggplants). The results of the study showed that simple linear regression was able to provide fairly accurate predictive results. This model can be used as a basis for decision making in production planning, supply chain management, and seed inventory management, thus supporting the efficiency of farming businesses and reducing potential losses due to mismatches between demand and supply.

Dempster Shafer Analysis in Mental and Emotional Health Monitoring Nasyuha, Asyahri Hadi; Sari, Dini Fakta; Azanuddin, Azanuddin; Khoiri, Muhammad Hafidz Ady
Paradigma - Jurnal Komputer dan Informatika Vol. 26 No. 2 (2024): September 2024 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v26i2.5044

Monitoring and diagnosing mental and emotional health is a significant challenge in the healthcare field due to its complex and subjective nature. This research aims to develop an expert system using the Dempster-Shafer method in monitoring and diagnosing mental and emotional health conditions. The Dempster-Shafer method was chosen because of its ability to handle uncertainty and combine various evidence from different information sources. This analysis is designed to identify seven types of mental and emotional illness by considering twenty-four related symptoms. The results of the analysis show that this expert system can provide a more accurate and comprehensive assessment compared to conventional methods. It is hoped that the implementation of this expert system can be an effective tool for medical personnel in making diagnoses and determining appropriate treatment steps for patients with mental and emotional health conditions. This study also highlights the potential of the Dempster-Shafer method in other applications that require evidence-based analysis under uncertainty.

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