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Journal Of Information System And Artificial Intelligence

jisai
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Published by Universitas Mercu Buana Yogyakarta

ISSN : - EISSN : 27976777 DOI : -

Core Subject : Science, Engineering,

Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

Journal of Information System and Artificial Intelligence (JISAI) diterbitkan oleh Program Studi Sistem Informasi, Fakultas Teknologi Informasi Universitas Mercu Buana Yogyakarta. JISAI memuat naskah hasil-hasil penelitian dibidang Sistem Informasi, Teknologi Informasi dan Sistem Komputer. JISAI berkomitmen untuk memuat artikel berbahasa Indonesia yang berkualitas dan dapat menjadi rujukan utama para akademisi, peneliti dan praktisi dalam bidang Sistem Informasi, Teknologi Informasi dan Ilmu Komputer. Jurnal ini diterbitkan 2 kali dalam 1 tahun yakni pada bulan November dan Mei dengan periode penerimaan artikel sepanjang tahun. 10 artikel pertama yang lolos seleksi akan diterbitkan pada periode penerbitan yang paling dekat. Sedangkan, artikel ke-11 dan seterusnya akan diterima untuk diterbitkan pada periode yang akan datang. Artikel yang masuk ke jurnal ini akan di-review oleh mitra bestari sebelum diterbitkan. Proses review artikel dilakukan secara double blind review yang mana mitra bestari tidak mengetahui siapa penulis artikel tersebut dan juga sebaliknya penulis tidak mengetahui mitra bestari yang menilai artikel tersebut. Jurnal JISAI merupakan jurnal akses terbuka (open access) sehingga seluruh artikel yang diterbitkan oleh jurnal ini dapat diakses kapan saja dan di mana saja oleh siapa saja tanpa dipungut biaya. Selain itu, untuk Submit dan Review Manuskrip adalah Bebas Biaya.

Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)

Articles 55 Documents

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Sistem Pakar Mendiagnosa Penyakit Pencernaan Pada Manusia Menggunakan Metode Forward Chaining dan Certainty Factor Arif Wijayanto; Indah Susilawati
Journal Of Information System And Artificial Intelligence Vol. 2 No. 1 (2021): Journal of Information System and Artificial Intelligence Vol II, No I November
Publisher : Universitas Mercu Buana Yogyakarta

Digestive disease is the most common disease and often found in health community centers. Digestive diseases attack organs in the digestive system which can interfere the functions of other systems. If ignored, it can make the person’s condition become more severe. To solve the problem, the researcher attempted to design a smart system to help people to early recognize digestive diseases such as GERD, dyspepsia, cholera, hepatitis, appendicitis, dysentery and hemorrhoids. Based on the symptoms fed by the patients into the system, the system will use the forward chaining method and certainty factor as an inference machine that will produce a disease diagnosis. Of 36 patient data tested on the system and matched with the experts’ validation, 34 of them matched. The system has an accuracy level of 94.4% of the tested data. The results are expected to provide an initial medium of consultation to help people diagnose digestive diseases. Keywords: Certainty Factor, Forward Chaining, digestive diseases, expert system.

Klasifikasi Jenis Aglaonema Berdasarkan Citra Daun Menggunakan Convolutional Neural Network (CNN) Yoga Purna Irawan; Indah Susilawati
Journal Of Information System And Artificial Intelligence Vol. 2 No. 2 (2022): Journal of Information System and Artificial Intelligence Vol II, No II Mei 202
Publisher : Universitas Mercu Buana Yogyakarta

Aglaonema or popularly known in Indonesia as "Sri Rejeki" is a leaf ornamental plant fancied by many people. This plant has unique leaves with beautiful and diverse shapes, colors, and patterns. Various ways can be used to identify this plant; one of which is by using an image processing technique in which the process is carried out through feature extraction or classification process. A method/algorithm to classify Aglaonema image is the Convolutional Neural Network (CNN). CNN is an algorithm of Deep Learning and is the development of a Multi Layer Perceptron (MLP). This study used the image of 5 types of Aglaonema leaves with 100 images of each type. The CNN model used in this study was the Alexnet model. Based on 4 experiments using the optimizer and different configurations of epoch values, the highest training validation accuracy value was 98.00%. The system also can classify Aglaonema images well with an accuracy success rate of 96% of 50 images tested.

Sistem Pemilihan Laptop Berdasarkan Kriteria Kebutuhan Dengan Metode Simple Multi Attribute Rating Technique (Smart) (Studi Kasus: Toko Laptop Guard Yogyakarta) Herli Setiawan; Arita Witanti
Journal Of Information System And Artificial Intelligence Vol. 2 No. 1 (2021): Journal of Information System and Artificial Intelligence Vol II, No I November
Publisher : Universitas Mercu Buana Yogyakarta

In the process of buying laptops, it is found out that customers at Laptop Guard Store are often confused when to select the specification of the laptop they need. They sometimes take a long time to choose the most appropriate laptop for them. The Simple Multi-Attribute Rating Technique (SMART) method is a decision support system that can help them select a laptop. This method starts with determining criteria, assigning criteria weight, normalizing criteria weight, determining sub-criteria, assigning sub-criteria weight, determining utility values, calculating total values, and drawing conclusions in the form of rankings or final recommendations. The criteria include college majors, brands, applications, processors, and prices. The results of this study will provide suggestions for laptop buyers to suit their needs for a laptop with a letter level of accuracy based on the needs calculation. The results of the accuracy testing of the system that have been made with calculations and is currently carried out by the company using 50 samples show an output of 100% relevant and 0% irrelevant. In this study, Asus laptops had the highest percentage of selection of 60%, Acer 34%, and Lenovo 6%. Keywords: laptop, decision support system, SMART.

Sistem Pakar Diganosa Jenis Kecanduan Narkoba Menggunakan Teorema Bayes Iqbal Adji Setiadhi
Journal Of Information System And Artificial Intelligence Vol. 2 No. 1 (2021): Journal of Information System and Artificial Intelligence Vol II, No I November
Publisher : Universitas Mercu Buana Yogyakarta

This research was to design an expert system to diagnose types of drug addiction using Bayes’ Theorem. Bayes' Theorem is a theorem used in statistics to calculate the probability of a hypothesis. The basis of the expert system knowledge is obtained from the acquisition of doctors’ knowledge. This study used 32 data of medical records. The medical records were incorporated into the system. The results of the system were matched with the experts to get the maximum matching number and a close identification result. Based on the 32 data tested against the experts and the system, the system could detect 4 types of drugs, namely Cannabis, Cocaine, Opioids, and Hallucinogens. There were 27 patients who were addicted to marijuana and they matched the experts’ validation, meanwhile 5 patients did not match the experts’ validation. The accuracy level of the system based on the results of the experts’ validation and the system was 84.38%.

Sistem Pakar Diagnosa Penyakit Kulit Akibat Gigitan Serangga Menggunakan Teorema Bayes Bagas Irvan Bagaskara; Agus Sidiq Purnomo
Journal Of Information System And Artificial Intelligence Vol. 2 No. 1 (2021): Journal of Information System and Artificial Intelligence Vol II, No I November
Publisher : Universitas Mercu Buana Yogyakarta

This research was to design an expert system to diagnose skin diseases caused by insect bites using Bayes’ Theorem. Bayes' Theorem is a theorem used in statistics to calculate the probability of a hypothesis. The basis of the expert system knowledge is derived from the acquisition of doctors’ knowledge. This study used 25 data of medical records. The medical records were implemented into the system. The results of the system were matched with the experts to get the maximum matching number and a close identification result. Based on the 25 patient data tested against the experts and the system, the system could detect 4 types of skin diseases caused by insect bites, namely Scabies, Insect Bite, Atopic Dermatitis, and Prurigo Simplex. Of the 25 patients who had skin diseases caused by insect bites and had been matched with the experts’ validation, 21 of them matched and 4 did not match. The accuracy level of the system based on the results of the experts’ validation and the system was 84%.

Mendeteksi Salak BerLarva dan Tidak BerLarva Menggunakan Metode Convolutional Neural Network Jati Nugroho; Supatman
Journal Of Information System And Artificial Intelligence Vol. 2 No. 1 (2021): Journal of Information System and Artificial Intelligence Vol II, No I November
Publisher : Universitas Mercu Buana Yogyakarta

Salacca fruit is one of the cultivars of the salak pondoh (the young fruits with sweet taste), which has become a leading commodity in Sleman, the Special Region of Yogyakarta Province. The salak madu (honey salacca fruit ~Eng.) became known when it was identified for the first time in Sempu Sub-village (Baterante), Wonokerto Village, Turi District, Sleman Regency. The most prominent feature of this salak madu is the leaves are shorter when compared to other types of salak pondoh. The color of the fruit's skin is blackish-brown when it is young, and it gradually becomes shiny brown after getting old. The arrangement of the scales forms a line pattern. This research was initiated because some people were sometimes deceived by the salak madu that looked good even though it had larvae inside. Therefore, the researcher was willing to create a system to detect which salacca fruits had larvae inside and which did not, using the Convolutional Neural Network (CNN) method. Detecting the salacca fruits with larvae and with no larvae required an image of the salacca fruits with larvae and with no larvae; after that, the image that had been obtained would go through a training process to detect the salacca fruits, whether they have larvae or no larvae; and after that, the image would be tested for the accuracy. The test result obtained by this System was 80%.

Deteksi Tingkat Kematangan Fermentasi Singkong (Tape Singkong) Menggunakan Convolutional Neural Network (CNN) Abdi Subayu; Supatman
Journal Of Information System And Artificial Intelligence Vol. 2 No. 2 (2022): Journal of Information System and Artificial Intelligence Vol II, No II Mei 202
Publisher : Universitas Mercu Buana Yogyakarta

Tapay is a food in which the manufacturing process involves yeast. Unlike others, tapay requires fermentation using yeast containing the Kapang Amylomyces Rousi, Mucor sp, Rhizopus sp, Khamir Saccharomycopsis fibuligera, Candida Utilis, Pichia burtonii, Saccharomyces Cerevisiae, Saccharomycopsis Malanga, and the bacteria Pediococcus sp and Bacillus sp. Cassava tapay (Manihot Utilissima) is food containing these elements. Problems arise when the common public has no idea about the ripeness of cassava fermentation. Therefore, an artificial neural system is developed to detect the ripeness of cassava fermentation using the Convolutional Neural Network (CNN) method. The CNN method is one of the Deep Learning methods that can carry out an independent learning process for object recognition that is extracted and classified, then can be applied to high-resolution images with a nonparametric distribution model. The study results by making 45 training data reached 96.88%, and using 30 cassava tapay test data reached 90%. These results aim to reduce community error, especially for consumers, in determining the ripeness of cassava tapay.

Aplikasi Sistem Pakar Diagnosis Risiko Penyakit Kanker Paru Dengan Metode Forward Chaining Berbasis Android Hendri Tri Cahya Leksana; Mutaqin Akbar
Journal Of Information System And Artificial Intelligence Vol. 2 No. 2 (2022): Journal of Information System and Artificial Intelligence Vol II, No II Mei 202
Publisher : Universitas Mercu Buana Yogyakarta

Early diagnosis of lung cancer, which is the number one cause of death in developed countries, is urgently needed. In general, lung cancer is caused by many things including an unhealthy environment, an unhealthy lifestyle and a lack of knowledge about the characteristics or symptoms that are signs that a person has a high or low risk of cancer. The acquisition of knowledge from experts, in this study, is an anatomical pathologist who specializes in obtaining a knowledge base. There are 50 selected respondents who are used as data in this study. The detectable risk is a low risk of lung cancer and a high risk of lung cancer. The forward chaining method is a method for calculating the confidence value of the symptoms given by a patient. The results of testing from 50 selected respondents got very good results for application assessment and application testing results are in accordance with the analysis of the informants by 70%.

Penerapan Supply Chain Management Untuk Mengoptimalkan Produksi Berdasarkan Persediaan Barang Murti Retnowo; Anita Fira Waluyo
Journal Of Information System And Artificial Intelligence Vol. 2 No. 2 (2022): Journal of Information System and Artificial Intelligence Vol II, No II Mei 202
Publisher : Universitas Mercu Buana Yogyakarta

Competitive competition and the arrival of global markets are challenges, these challenges are related to getting products and services that are on time and at a low cost. Organizations or companies begin to realize this and to deal with it is not enough just to increase efficiency in organizations or companies, but the entire supply chain must be competitive. Increasing competition between companies in both local and international markets makes managers determined to focus on how to gain competitive advantage in order to stay in business. At present the achievement of fulfilling customers and getting their trust becomes more and more difficult. This can be obtained through the improvement of products and services, to fulfill both of them, one of which is the application of Supply Chain Management (SCM) and Outsourcing, SCM and Outsourcing methods have been recognized as ways to gain competitive advantage

Sistem Pakar Diagnosa Penyakit Pada Tanaman Cabai Merah Menggunakan Metode Certainty Factor Dan Weighted Berbasis Web Sandra Ariesta Indarwati; Indah Susilawati
Journal Of Information System And Artificial Intelligence Vol. 2 No. 2 (2022): Journal of Information System and Artificial Intelligence Vol II, No II Mei 202
Publisher : Universitas Mercu Buana Yogyakarta

Cabai merah adalah jenis sayuran yang dapat membangkitkan selera makan khususnya di lidah pencinta kuliner pedas di Indonesia. Budidaya tanaman cabai banyak ditemui di Indonesia salah satunya di daerah Kabupaten Blitar. Pada musim penghujan, para petani selalu merasa resah pada saat menanam cabai di musim hujan. Pada musim hujan cabai rentan terkena penyakit yang cukup banyak. Untuk mengatasi penyakit tersebut, dibutuhkan langkah yang tepat yaitu dengan cara memberikan penanganan khusus berupa pengobatan yang benar terhadap tanaman yang terjangkit penyakit.Untuk proses mendiagnosa jenis-jenis penyakit yang ada di tanaman tersebut maka diperlukan cara alternatif dengan membuatkan sebuah aplikasi sistem pakar agar dapat mendiagnosa penyakit pada tanaman cabai. Dalam rancangan sistem pakar ini, menerapkan metode Certainty Factor dan Weighted Product. Metode Certainty Factor digunakan untuk menghitung nilai bobot saat gejala awal penyakit pada tanaman cabai. Sedangkan metode Weighted Product adalah proses menormalisasi dalam menentukan jenis penyakit berdasarkan perhitungan bobot gejala awal penyakit. Dalam penelitian ini dilakukan pengujian terhadap user untuk melakukan konsultasi dengan menjawab beberapa pertanyaan tentang gejala- gejala yang telah dialami, berdasarkan hasil pengujian yang telah dilakukan pada sistem pakar sebanyak 30 kali, nilai akurasi yang didapat adalah sebanyak 90.48% dimana ini membuktikan sistem sudah berjalan dengan baik dan memiliki tingkat akurasi yang tinggi dalam menganalisis penyakit pada tanaman cabai.

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Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name
Albert Yakobus Chandra

Contact Email
albert.ch@mercubuana-yogya.ac.id

Phone
+6285239280085

Journal Mail Official
jisai@mercubuana-yogya.ac.id

Editorial Address
Jl.Jembatan Merah, No.84C, Gejayan, Yogyakarta

Location
Kab. bantul,

Daerah istimewa yogyakarta

INDONESIA

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name Albert Yakobus Chandra

Contact Email albert.ch@mercubuana-yogya.ac.id

Phone +6285239280085

Journal Mail Official jisai@mercubuana-yogya.ac.id

Editorial Address Jl.Jembatan Merah, No.84C, Gejayan, Yogyakarta

Location Kab. bantul, Daerah istimewa yogyakarta INDONESIA

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Contact Name
Albert Yakobus Chandra

Contact Email
albert.ch@mercubuana-yogya.ac.id

Phone
+6285239280085

Journal Mail Official
jisai@mercubuana-yogya.ac.id

Editorial Address
Jl.Jembatan Merah, No.84C, Gejayan, Yogyakarta

Location
Kab. bantul,

Daerah istimewa yogyakarta

INDONESIA