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Journal of Intelligent Software Systems
Published by Universitas Teknologi Digital Indonesia
ISSN : -     EISSN : 29627702     DOI : https://doi.org/10.26798/jiss
Core Subject : Science,
Computer Science & IT
Journal of Intelligent Software Systems (JISS) is open access, peer-reviewed international journal that will consider any original scientific article that expands the field of Intelligent Software Systems. The journal publishes articles in all Intelligent Software Systems specialities of interest to Intelligent Software Systems, physicians, and researchers.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 28 Documents
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ETL DATA WAREHOUSE ON PERFORMANCE MEASUREMENT Linda Sutriani; Indra Yatini Buryadi; Hera Wasiati; Sudarmanto Sudarmanto
Journal of Intelligent Software Systems Vol 2, No 1 (2023): July
Publisher : LPPM UTDI (d.h STMIK AKAKOM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiss.v2i1.928

Abstract

Measuring the organizational performance correctly will turn out accurate data which can be analyzed to provide important information for management in making decisions to improve company performance. This is because in the past, the performance appraisal was only based on financial measures. However, the editor-in-chief needs more than just financial indicators to improve performance. The Balanced Scorecard measures four organizational dimensions, such as customer, financial, internal business, and learning and growth. Although originally designed for the private sector, many public organizations apply it as modifications to suit their needs. The Balanced Scorecard is an effective method for measuring and achieving organizational performance. This research involves ETL in the Balanced Scorecard process to combine data from various sources into a large central repository called a data warehouse
Mushroom Image Classification Using C4.5 Algorithm Cucut Hariz Pratomo; Widyastuti Andriyani
Journal of Intelligent Software Systems Vol 2, No 1 (2023): July
Publisher : LPPM UTDI (d.h STMIK AKAKOM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiss.v2i1.930

Abstract

This study applied five types of Mushrooms, they are Button mushrooms, Wood Ear mushrooms, Straw mushrooms, Reishi mushrooms and Red Oyster mushrooms. The feature extraction used is Order 1 with the parameters of mean, skewness, variance, kurtosis, and entropy. The process carried out to identify mushroom images by preparing image objects. There were 15 images of each mushroom class were taken for each mushroom and stored in .jpg format. The image processing is carried out by a feature extraction process. Then five images for each mushroom class are chosen. They were used as test images which will be classified so that identification results are obtained. This study applies the Classification Algorithm C4.5 to build a decision tree, which will also identify the results of the accuracy of processed mushroom images. The obtained result of accuracy was 84% in the classification of feature extraction Order 1
ANALYSIS AND DESIGN OF DATA WAREHOUSE AND DATA MART BUDGET Hendra Maryanto; Bambang Purnomosidi Dwi Putranto; Rikie Kartadie; Muhammad Guntara; Robertus Saptoto
Journal of Intelligent Software Systems Vol 2, No 1 (2023): July
Publisher : LPPM UTDI (d.h STMIK AKAKOM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiss.v2i1.927

Abstract

University as higher education institutions must be able to manage budgets properly. The budget is a future financial plan which includes the expectations of university management. This research will design a data warehouse, which is a place where data can be stored on a large scale. In this research, a data warehouse will be designed as a place to store budget data. The method applied in this study is the Kimball method with a nine-step methodology. The result of this research is a data warehouse design and budget data mart
IOT BASED SOIL MOISTURE MONITORING AND SOIL MOISTURE PREDICTION USING LINEAR REGRESSION (CASE STUDY OF VINCA PLANTS) Kuindra Iriyanta; Bambang Purnomosidi Dwi Putranto; Widyastuti Andriyani
Journal of Intelligent Software Systems Vol 2, No 1 (2023): July
Publisher : LPPM UTDI (d.h STMIK AKAKOM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiss.v2i1.929

Abstract

Soil moisture is something that becomes important. Indonesia as an agricultural country, most of the population has a profession as a farmer. In agriculture, one of the important parts is the water composition in the soil or soil moisture. One attempt to maintain soil moisture is to provide sufficient water intake to the soil. However, in practice, it is sometimes complicated for farmers to do proper irrigation of their agricultural land. This humidity condition will ultimately determine the success of vinca plant cultivators. The accuracy of giving water both in terms of time management and volume are two things which are an important focus of vinca crop growing. This system is designed using a humidity sensor which is used to measure the moisture composition contained in the soil, and an air temperature sensor. The NodeMCU ESP2866 microcontroller acts as a link between Google spreadsheet sensors. NodeMCU ESP2866 will send humidity and temperature sensor reading data to Google spreadsheets using a RESTfull API which can connect one application to another. The sensor data is then saved to Google spreadsheet and processed using the linear regression method. The processing results will be displayed on the Google Data Studio dashboard. The output of this process is to provide information about soil moisture conditions, notification of soil moisture conditions if it is too dry or damp, thus the prevention of the death of vinca plants can be carried out. The benefit for users is that they can carry out periodic and real-time monitoring by simply using the Telegram instant messaging application, which is expected to reduce the risk of plant death due to drought or excessive watering
Polynomial Regression Method and Support Vector Machine Method for Predicting Disease Covid-19 in Indonesia Bambang Purnomosidi Dwi Putranto; Moh. Abdul Kholik; Muhammad Agung Nugroho; Danny Kriestanto
Journal of Intelligent Software Systems Vol 2, No 1 (2023): July
Publisher : LPPM UTDI (d.h STMIK AKAKOM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiss.v2i1.931

Abstract

The COVID-19 pandemic has become a major threat to the entire country. According to the WHO report, COVID-19 is a severe acute respiratory syndrome transmitted through respiratory droplets resulting from direct contact with patients. This study of data history is then processed using data mining prediction methods, namely the Polynomial Regression method compared to the Support Vector Machine method. Of the two methods will be sought the most accurate method by testing accuracy with MAE, MSE, and also MAPE to get the results of covid-19 predictions in Indonesia. Based on the comparison of test results through various scenarios against both methods, the Polynomial Regression method obtained the smallest test value, resulting in an accuracy value of MAE = 4146.025749867596, MSE = 19031800.02642069, MAPE = 0.006174164877416524. Polynomial regression is the best-recommended method
Microservices Architecture in Point of Sales Application Based on Restful API and Webhook Rizki Arif Setiadi; Bambang Purnomosidi; Widyastuti Andriyani Andriyani; Sri Rezeki Candra Nursari
Journal of Intelligent Software Systems Vol 3, No 1 (2024): July 2024
Publisher : LPPM UTDI (d.h STMIK AKAKOM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiss.v3i1.1336

Abstract

Layanan mikro adalah kumpulan proses kecil dan independen yang berkomunikasi dengan satu sama lain untuk membuat aplikasi kompleks yang tidak bergantung pada bahasa API tertentu. Dalam penelitian ini, penulis berupaya mengimplementasikan Microservices Architecture, RESTful API, dan Webhook pada aplikasi Point of Sale untuk mencapai sistem dengan kinerja, kecepatan, dan skalabilitas yang unggul, terutama dalam pertukaran dan komunikasi data.  Penelitian ini menggabungkan dua metodologi pengujian penting, yaitu unit pengujian dan pengujian fungsional, untuk memastikan ketahanan dan keandalan aplikasi Point of Sale berbasis Microservices. Unit pengujian fokus pada validasi masing-masing komponen dan fungsi dalam sistem, sedangkan pengujian fungsional menilai fungsionalitas dan perilaku aplikasi secara keseluruhan. Pendekatan pengujian ini bertujuan untuk meningkatkan kualitas dan kehalusan sistem.  Temuan penelitian ini menunjukkan bahwa implementasi Microservices Architecture berbasis RESTful API dan Webhook berhasil meningkatkan akurasi input data pada aplikasi Point of Sale yang dibuktikan dengan perhitungan pada konfusi matriks. Sebelum diterapkan webhook, akurasinya hanya 80 persen, namun setelah diterapkan, akurasinya meningkat hingga 100 persen
A Decision Model to Support the Selection of SENKOM Personnel Using the Profile Matching Method with the Capability of Cyber Security I Nyoman Oka Semadi; Domy Kristomo; Bambang Purnomosidi
Journal of Intelligent Software Systems Vol 2, No 2 (2023): December 2023
Publisher : LPPM UTDI (d.h STMIK AKAKOM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiss.v2i2.1135

Abstract

The very rapid development of information technology has brought tactical and strategic advantages, but it can also be a potential attack from opposing parties on the information and communication systems and networks used, thus opening the way for the emergence of a new war, namely cyber warfare. Cyber attacks are a new threat to Adisutjipto Air Base, which targets vital parts that can impact the organization and make the command and control system ineffective and inefficient. One of the important elements of Adisutjipto Lanud in facing cyber attacks is the readiness of data and communication network security personnel. In the direct or conventional personnel selection process, it is not possible to see the abilities possessed by prospective data security personnel, both in terms of skills, management aspects, analytical aspects, competency weight, and so on. A decision support system can be used to assist decision-making based on existing criteria. This research is limited to only considering the selection of personnel who will become members of komlek or senkom who are responsible for data security and communications networks at Adisutjipto Air Base. In this research, the method used is the profile matching method. The concept of the profile matching method is to compare the selection using the conventional method with the decision support system method in selecting komlek/senkom personnel as cyber security personnel so that differences in competency can be identified, also called GAP (Gross Across Product). The smaller the GAP produced, the greater the weight of the value. large, this means that personnel who meet the requirements have a greater chance of someone occupying that position. The final result of this research is to obtain ranking information for each cyber security candidate based on profile matching calculations to be able to carry out tasks optimally in securing data and networks at Adisutjipto Air Base.
Analyzing Indonesian Football Sentiment Towards PSSI Performance Using Support Vector Machines Faturrahman Hakim; Yuli Astuti
Journal of Intelligent Software Systems Vol 3, No 1 (2024): July 2024
Publisher : LPPM UTDI (d.h STMIK AKAKOM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiss.v3i1.1330

Abstract

Football is a popular and widely engaged sport in Indonesia, attracting individuals across various age groups, including teenagers, adults, and children. The Indonesian Football Association (PSSI), established on April 19, 1930, originally named the All-Indonesian Football Association, is the governing body responsible for managing and overseeing football activities in the country. Despite its long history, PSSI has faced significant criticism for its perceived lack of professionalism in handling and managing Indonesian football. This discontent was notably amplified in the wake of the cancellation of the U-20 World Cup, leading to a surge of negative sentiments on social media platforms, particularly Twitter. This study aims to analyze public opinion regarding PSSI's performance. Public opinion, which emerges in response to various events, tends to be diverse due to the differing perspectives of individuals. The research focuses on assessing the balance between positive and negative sentiments towards PSSI's performance. By employing a comprehensive approach to sentiment analysis, including stages such as data preprocessing, labeling, modeling, and evaluation, this study provides a detailed examination of public sentiment. The methodology involves the application of the Support Vector Machine (SVM) algorithm across four tests with different data splits and the use of the SMOTE technique to address class imbalance. The findings reveal that the fourth test yielded the most effective results in sentiment classification, achieving an accuracy of 70.75\%, precision of 67.16\%, recall of 68.18\%, and an F1 score of 67.66\%
Rule Based System to Support Decisions on Determining Employee Status (Lecturers) for Scholarship Student Graduates Hotma Sadariahta Sipayung; Widyastuti Andriyani; Bambang Purnomosidi Dwi Putranto; Danny Kriestanto
Journal of Intelligent Software Systems Vol 3, No 1 (2024): July 2024
Publisher : LPPM UTDI (d.h STMIK AKAKOM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiss.v3i1.1337

Abstract

Salah satu permasalahan yang terjadi di Universitas Teknologi Digital Indonesia (UTDI) adalah proses seleksi yayasan Dosen Tetap yang disebut-sebut baru untuk diterapkan kepada mahasiswa penerima calon beasiswa S2 di Magister Teknologi Informasi (MTI). UTDI Yogyakarta. Kriteria yang digunakan dalam aturan tersebut adalah Indeks Prestasi (IP) Semester 1, IP Semester 2, IP Semester 3, Indeks Prestasi Kumulatif (IPK), Makalah (karya ilmiah), Kerjasama, Disiplin, Komunikasi, Pra Tesis, Tesis, Nilai C. , dan Durasi Studi yang diperoleh dari MTI UTDI, selanjutnya akan menggunakan Algoritma C4.5 untuk menghasilkan pohon keputusan yang akan dipelajari aturan dalam sistem. Penelitian ini menggunakan kaidah yang diperoleh dari MTI UTDI oleh Ketua Program Studi (Kaprodi) yaitu 41 data latih dan 8 data uji. Menggunakan forward chaining sebagai metode dalam sistem pakar yang mencari solusi melalui permasalahan, kemudian menggunakan Algoritma C4.5 yang merupakan algoritma yang digunakan untuk membentuk pohon keputusan. Aturan yang terbentuk kemudian digunakan untuk memprediksi kelayakan lulusan beasiswa Magister menjadi Dosen Tetap, Dosen Kontrak, atau tidak memenuhi persyaratan. Hasil prediksi tersebut kemudian dievaluasi menggunakan Confusion Matrix dan memperoleh nilai akurasi sebesar 75%, Precision sebesar 77,78%, dan Recall sebesar 77,78%. Sehingga Algoritma C4.5 dengan menggunakan aplikasi RapidMiner cukup layak digunakan untuk mendukung pengambilan keputusan dalam pemilihan mahasiswa penerima beasiswa Magister yang akan diangkat menjadi Dosen Tetap, Dosen Kontrak maupun yang tidak memenuhi syarat sebagai Dosen di UTDI. Fakultas Teknologi Informasi
Twitter Sentiment Analysis Classification to Assess Public Opinion on Football Matches Using the Naïve Bayes Method Yuli Astuti; Hafiidh Khoiru Pradana; Dewi Anisa Istiqomah; supriatin supriatin; Ninik Tri Hartati
Journal of Intelligent Software Systems Vol 2, No 2 (2023): December 2023
Publisher : LPPM UTDI (d.h STMIK AKAKOM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiss.v2i2.1136

Abstract

 The Kanjuruhan tragedy has attracted many comments on various social media platforms. This research will compare the number of positive and negative comments on Twitter and social media and determine the accuracy of the classification method used. The data used in this study consisted of 2052 pieces, consisting of 1015 positive and 1037 negative pieces. To determine the effect of the amount of training data on the resulting accuracy, testing will be carried out three times with different combinations of training data and test data, namely 70:30, 80:20, and 90:10. The results of this study obtained the highest accuracy value of 79.6%. This program can be developed for other social media platforms such as Facebook, Instagram, and others

Page 2 of 3 | Total Record : 28

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