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All Journal Techno.Com: Jurnal Teknologi Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) International Journal of Advances in Intelligent Informatics Register: Jurnal Ilmiah Teknologi Sistem Informasi JOIV : International Journal on Informatics Visualization JurTI (JURNAL TEKNOLOGI INFORMASI) JISKa (Jurnal Informatika Sunan Kalijaga) Journal of Informatics and Science Media (JISMEDIA) Transaction on Informatics and Data Science
Suhartono
Universitas Islam Negeri Maulana Malik Ibrahim Malang
Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Library & Information Science

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Identification of Canaries Bird’s Chirp Quality Using Statistic Analysis, Sound Analysis and Fuzzy Mamdani Method Suhartono Suhartono
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 2: April 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i2.8537

Abstract

Research about sound processing by computer using fuzzy logic has been known since 1970. One of approach logic fuzzy method is fuzzy mamdani method. Fuzzy mamdani method is the method to give conclusion from groupof rules of fuzzy. There have to be minimum of two rules, input rule and output rule. Sound processing in canaries bierd’s chirp quality can be explained as measurement standar for canary’s bird’s chirp to the point of song variant and volume. The background of this research is to create a sound identification system that uses dynamic data, the pattern of canary’s bird’s chirp obtained from dynamic data.Dynamic data is difficult to approach with certain formulas. The purpose of this research is to create indentification system to measure Canaries bird’s chirp quality pre-contest. The method used in this research was statistic analysis, sound analysis and fuzzy Mamdani method. Statistic analysis was used to look for important features from Canarie’s chirp sample. This analysis results Max amplitude variable, Min amplitude variable, Root-mean square. Then sound analysis results Autocorrelation time, Zero cross and Energy. Then those values were used as the input in fuzzy Mamdani method process. As for the output variables were the judges score result about the quality of bird’ chirp. The results from identification system of bird’s chirp quality from 6 samples are (1). Accuration level 81,67%. (2) Error sytemrate 18,33%. (3). Based on system performance and error rate that have been known can be concluded that the system can indentifyCanarie’s chirp quality well.
Identification of virtual plants using bayesian networks based on parametric L-system Suhartono Suhartono; Fachrul Kurniawan; Bahtiar Imran
International Journal of Advances in Intelligent Informatics Vol 4, No 1 (2018): March 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v4i1.157

Abstract

Parametric L-System is a method for modelling virtual plants. Virtual plant modelling consists of components of axiom and production rules for alphabets in parametric L-System. Generally, to get the alphabet in parametric L-System, one would guess the production rules and perform a modification on the axiom. The objective of this study was to build virtual plant that was affected by the environment. The use of Bayesian networks was to extract the information structure of the growth of a plant as affected by the environment. The next step was to use the information to generate axiom and production rules for the alphabets in the parametric L-System. The results of program testing showed that among the five treatments, the combination of organic and inorganic fertilizer was the environmental factor for the experiment. The highest result of 6.41 during evaluation of the virtual plant came from the treatment with combination of high level of organic fertilizer and medium level of inorganic fertilizer. Mean error between real plant and virtual plan was 9.45 %.
Prediksi Kategori Kelulusan Mahasiswa Menggunakan Metode Regresi Logistik Multinomial Rafika Syahranita; Suhartono Suhartono; Syahiduz Zaman
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 8 No. 2 (2023): Mei 2023
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2023.8.2.102-111

Abstract

Students must meet certain goals to earn a degree but can extend their time at university or drop out (DO). The problem of dropping out of students has become an important issue for tertiary institutions to ensure the success or graduation of students and reduce dropouts. DO can affect the accreditation of the tertiary institution. The quality of higher education institutions in Indonesia is measured based on accreditation from the National Accreditation Board for Higher Education or BAN-PT. One of the main standards measured is the Quality of Students and Graduates. The quality of educational accreditation is measured by the percentage of student graduation and the university's strategy to retain students. To predict student graduation based on graduation time categories, researchers collected academic data from students in 2012-2018 at the Informatics Engineering Study Program, State Islamic University of Maulana Malik Ibrahim Malang. The variables used as predictors are gender, type of entry pathway, and grade point average from semesters one to six. The resulting model was evaluated to obtain an accuracy value of 85.5%, a precision of 78.5%, a recall of 93.9%, and a micro f1-score of 89.8%. An accuracy value of 85.5% indicates that the system can classify properly using the logistic regression model.
Bidirectional GRU dengan Attention Mechanism pada Analisis Sentimen PLN Mobile Moh. Ainur Rohman; - Suhartono; Totok Chamidy
Techno.Com Vol 22, No 2 (2023): Mei 2023
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/tc.v22i2.7876

Abstract

PLN Mobile adalah aplikasi ponsel customer self-service yang terintegrasi dengan Aplikasi Pengaduan dan Keluhan Pelanggan (APKT) dan Aplikasi Pelayanan Pelanggan Terpusat (AP2T). Mulai awal tahun 2021 sampai sekarang PLN menggencarkan sosialisasi PLN Mobile pada masyarakat sehingga jumlah ulasan PLN Mobile pada google playstore meningkat drastis. Untuk mengetahui kepuasan pelanggan tidak bisa hanya dengan melihat dan menganalisis dari kolom ulasan PLN Mobile di google playstore, hal ini dikarenakan data ulasan berbentuk tidak terstruktur. Untuk mengatasi masalah ini dibutuhkan teknik khusus yaitu analisis sentimen. Penelitian ini bertujuan untuk mengusulkan arsitektur analisis sentimen untuk mengatasi ketidakmampuan algoritma deep learning seperti LSTM dan GRU dalam menangkap informasi penting. Arsitektur yang diusulkan yaitu mengkombinasikan Bidirectional GRU (BiGRU) dengan attention mechanism menggunakan word2vec sebagai word embedding. Attention mechanism digunakan untuk menangkap kata yang penting sehingga arsitektur tersebut dapat memahami informasi yang penting. Kemudian, arsitektur yang diusulkan dilakukan perbandingan dengan metode CNN, CNN-GRU, CNN-LSTM, CNN-BiGRU, CNN-BiLSTM dengan menggunakan data ulasan PLN Mobile. Hasil eksperimen menunjukkan bahwa arsitektur analisis sentimen yang diusulkan memiliki akurasi dan f1-score yang lebih tinggi.
Enhanced PBFT Blockchain based on a Combination of Ripple and PBFT (R-PBFT) to Cryptospatial Coordinate Achmad Teguh wibowo; Mochamad Hariadi; Suhartono Suhartono; Muhammad Shodiq
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol. 8 No. 2 (2022): July
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v8i2.3041

Abstract

In this research, we introduce the combination of two Blockchain methods. Ripple Protocol Consensus Algorithm (RPCA) and Practical Byzantine Fault Tolerance (PBFT) are applied to cryptospatial coordinates to support cultural heritage tourism. The PBFT process is still used until the preparation process to ensure a maximum error of 33%, and every node would add a new chain in all nodes, so PBFT has a slower processing speed than other methods. This research cuts the PBFT process. After the preparation process in PBFT, the data was entered into the RPCA node and was calculated using an equation to minimize errors with a maximum limit of 20%. After this process, the was were sent to the commit process to store the data in all connected nodes in the Blockchain network; we call this combination of two methods R-PBFT. Combining the two methods can enhance data processing security and speed because it still uses the PBFT work combined with the speed of RPCA. Furthermore, this method uses a fault tolerance value from the RPCA of 20% to enhance data processing security and speed.
Prediction of State Civil Apparatus Performance Allowances Using the Neural Network Backpropagation Method Puan Maharani Kurniawan; Agung Teguh Wibowo Almais; M. Amin Hariyadi; M. Ainul Yaqin; Suhartono Suhartono
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1698

Abstract

Performance allowance is a form of appreciation given by an agency to its human resources. The Office of the Ministry of Religion of Batu City provides performance allowances to civil servants who work in the agency. Several things that affect the provision of performance allowances, such as grade, deduction, taxable income, income tax, and total tax, are used in this study to produce the total gross performance allowances and total performance allowances received. Based on the data obtained, there are some missing data from the parameters of taxable income, income tax, and total tax. This study aims to predict performance allowance when there is missing data. The method used is Neural Network Backpropagation. This study uses 480 data with split data ratios of 50:50, 60:40, 70:30, and 80:20, with epochs 40,000 and a learning rate 0,9. Four types of models used in this study are distinguished based on the number of hidden layers and epochs used. Model A uses two hidden layers to produce the highest accuracy with a 50:50 data split ratio of 65,16%. Model B uses four hidden layers to produce the highest accuracy with a 50:50 data split ratio of 69,34%. Model C uses six hidden layers to produce the highest accuracy with a 50:50 data split ratio of 68,18%. Model D uses eight hidden layers to produce the highest accuracy with a 50:50 data split ratio of 70,90%.
Klasifikasi Sentimen Masyarakat Terhadap Proses Pemindahan Ibu Kota Negara (IKN) Indonesia pada Media Sosial Twitter Menggunakan Metode Naïve Bayes Moch. Reinaldy Destra Fachreza; Suhartono Suhartono; M. Ainul Yaqin
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 8 No. 3 (2023): September 2023
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2023.8.3.243-251

Abstract

Some time ago, the House of Representatives passed Law (UU) Number 3 of 2022 concerning the National Capital City on January 18, 2022. Then, President Joko Widodo officially signed the IKN Law on February 15, 2022. Thus, the Indonesian capital will be moved to Penajam Paser Utara Regency and Kutai Kartanegara Regency, East Kalimantan Province. The public's response to the decision varies; many respond with supportive sentiments, but some react with unsupportive ideas. Nowadays, there are many ways to observe information collected on social media. Various responses submitted through social media can be used as sentiment classification research data. The Naïve Bayes method is commonly used for this type of research. Data was collected between February 15-25, 2023, with as many as 500 tweets. This research uses the Gaussian Naïve Bayes type because of the independence assumption made by this method. Features that do not significantly contribute to the classification can be ignored, thus reducing the impact of irrelevant features. This study aims to measure public sentiment on Twitter towards the process of moving the nation's capital. The system created provides the best trial results at 80% feature usage with 82.0% accuracy, 76.9% precision, and 100% recall.
Perancangan Website Tracking Surat dengan Metode Design Thinking Kartika Wulandari; Suhartono Suhartono
JurTI (Jurnal Teknologi Informasi) Vol 7, No 2 (2023): DESEMBER 2023
Publisher : Universitas Asahan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36294/jurti.v7i2.3706

Abstract

Penelitian ini membahas tentang pengembangan website tracking surat sebagai solusi untuk meningkatkan efisiensi dan transparansi dalam pengelolaan surat dan dokumen di Perusahaan XYZ. Dengan menerapkan metodologi Design Thinking, penelitian ini melibatkan tahap Empathize, Define, Ideate, Prototype, dan Test. Tahap Empathize melibatkan wawancara dan observasi dengan pengguna untuk memahami permasalahan yang ada. Pada tahap Define, isu-isu utama diidentifikasi dan tujuan proyek ditetapkan. Kemudian, tahap Ideate menghasilkan berbagai ide solusi. Sebuah prototipe antarmuka situs web dibuat pada tahap Prototype, dan kemudian diuji dengan pengguna yang sebenarnya pada tahap Test. Temuan dari penelitian ini adalah sebuah platform website yang memungkinkan pengguna untuk melacak surat, mengelola data surat, dan melaporkan surat yang tidak terkirim. Hasil dari penelitian ini adalah website yang digunakan oleh PT XYZ yang mengintegrasikan desain inovatif dan teknologi informasi untuk meningkatkan efisiensi dan transparansi dalam pengelolaan surat, yang berpotensi memberikan dampak positif bagi operasional perusahaan.
Bibliometrik Menggunakan Vosviewer dengan Publish or Perish: Penelitian Neural Network dalam Klasifikasi Penyakit Tanaman Nova Rahma; Imamatul Khoiriyah; Suhartono
Jurnal Informatika dan Sains Media (JISMA) Vol 1 No 1 (2024): Vol. 1 No. 1 (2024) July
Publisher : Yayasan Amanah Putra Mandiri

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

Abstract

Plant diseases pose a severe threat to global food security and agricultural sustainability. The impact of plant diseases causes economic losses for farmers and food shortages for the population. To overcome this problem, research in agriculture utilizes advanced technologies such as neural networks to detect plant diseases. This study aims to understand the development of research on using neural networks in plant disease classification. This method used the Neural Network method to identify plant diseases from images, with a bibliometric analysis of 72 articles published between 2019 and 2024. The results of this study map the network of knowledge and collaboration in the use of neural networks for plant disease classification, identifying three main clusters that reflect the research focus and application of this technology. It was concluded that this study successfully understood the research developments related to using neural networks in plant disease classification.
Naive Bayes Classification for Software Defect Prediction Edwin Hari Agus Prastyo; Muhammad Ainul Yaqin; Suhartono; M. Faisal; Reza Augusta Jannatul Firdaus
Transactions on Informatics and Data Science Vol. 1 No. 1 (2024)
Publisher : Department of Informatics, Faculty of Da'wah, UIN Saizu Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24090/tids.v1i1.12192

Abstract

Software defects are an inevitable aspect of software development, exerting substantial influence on the reliability and performance of software applications. This research addresses the imperative need to enhance the prediction and monitoring of software defects within the software development domain. With a focus on system stability and the prevention of software malfunctions, this study underscores the significance of proactive measures, including robust software testing, routine maintenance, and continuous system monitoring. The central challenge addressed in this research pertains to the insufficient efficiency of predicting software defects during the development phase. To address this challenge, the study employs the Naive Bayes classification method. Test results conducted on the complete dataset reveal that the Naive Bayes method yields classifications with an exceptionally high accuracy rate, reaching 98%. These findings suggest that the method holds great potential as an effective tool for predicting and preventing software defects throughout the software development process. Additionally, through linear regression analysis, the model exhibits an intercept value of -0.09359968 and a coef coefficient of 0.00761893. The outcomes of this research bear significant implications for the implementation of the Naive Bayes method in software bug prediction analysis, particularly in the utilization of the Python programming language with the assistance of Google Colab. The adoption of this method can play a pivotal role in mitigating risks and elevating the overall quality of software during the developmental stages.
Co-Authors Achmad Teguh Wibowo Agung Teguh Wibowo Almais Edwin Hari Agus Prastyo Fachrul Kurniawan Imamatul Khoiriyah Imran, Bahtiar Kartika Wulandari M. Ainul Yaqin M. Ainul Yaqin M. Faisal Moch. Reinaldy Destra Fachreza Mochamad Hariadi Moh. Ainur Rohman Mokhamad Amin Hariyadi Muhammad Ainul Yaqin Muhammad Shodiq Nova Rahma Puan Maharani Kurniawan Rafika Syahranita Reza Augusta Jannatul Firdaus Syahiduz Zaman Totok Chamidy