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All Journal MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Jurnal Teknologi dan Manajemen Informatika Prosiding Seminas Competitive Advantage Jurnal Teknologi Informasi dan Ilmu Komputer Proceeding of the Electrical Engineering Computer Science and Informatics Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer JOIV : International Journal on Informatics Visualization EnviroScienteae Syntax Literate: Jurnal Ilmiah Indonesia Indonesian Journal of Artificial Intelligence and Data Mining Applied Information System and Management Technomedia Journal MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JURIKOM (Jurnal Riset Komputer) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Mantik JISKa (Jurnal Informatika Sunan Kalijaga) Jurnal Mnemonic G-Tech : Jurnal Teknologi Terapan Jurnal Cahaya Mandalika Jurnal Aplikasi Teknologi Informasi dan Manajemen (JATIM) The Indonesian Journal of Computer Science International Journal of Information Systems and Technology (IJOINT)
Cahyo Crysdian
Universitas Islam Negeri Maulana Malik Ibrahim Malang
Aerospace Engineering Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Social Sciences Other

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REVIEW PENERAPAN METODE KLASIFIKASI PADA SISTEM REKOMENDASI SOSIAL KEMASYARAKATAN Tri Gunantohadi; Cahyo Crysdian
Jurnal Aplikasi Teknologi Informasi dan Manajemen (JATIM) Vol 3 No 2 (2022): Jurnal Aplikasi Teknologi Informasi dan Manajemen (JATIM) Oktober 2022
Publisher : Universitas Islam Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31102/jatim.v3i2.1578

Abstract

Perkembangan Teknologi Informasi saat ini sangat cepat seiring dengan perkembangan jaman. Salah satu yang dihasilkan dari teknologi informasi adalah sistem rekomendasi. Sistem rekomendasi sangatlah penting dalam pengambilan keputusan karena sudah menggunakan berbagai acuan atau parameter yang sesuai. Seperti pada perusahaan atau Lembaga sangat penting dalam menentukan keputusan yang didasari oleh rekomendasi. Misalnya penentuan beasiswa bagi siswa atau mahasiswa, penentuan bantuan sosial kepada masyarakat tidak mampu harus tepat sasaran, begitu juga perusahaan dalam menentukan promosi jabatan bagi karyawan. Hal tersebut secara langsung akan melibatkan manajemen SDM untuk menseleksi karyawan yang sesuai kompetensinya sehingga mendapatkan posisi jabatan yang sesuai dengan bidangnya. Teknologi Informasi dapat membantu dalam proses memberikan rekomendasi yaitu dengan menggunakan Machine Learning (ML). Machine Learning sangat membantu dalam menghasilkan sistem rekomendasi. Penelitian ini menggunakan studi literatur dengan menggunakan jurnal-jurnal tentang rekomendasi dan metode klassifikasi. Untuk mendapatkan algoritma yang sesuai dilakukan perbandingan hasil pengujian dari jurnal-jurnal yang digunakan. Metode klasifikasi digunakan didalam penelitian ini dengan tujuan untuk menentukan keputusan dengan melalui sistem rekomendasi. Hasil penelitian ini dihasilkan sistem rekomendasi yang dibuat menggunakan algoritma Naive Bayes dengan nilai akurasi sebesar 95,11% [10] dan C4.5 dengan nilai akurasi sebesar 99.03% [14] karena nilai akurasi pada metode tersebut memiliki nilai yang tertinggi
Klasifikasi Ulasan Fasilitas Publik Menggunakan Metode Naïve Bayes dengan Seleksi Fitur Chi-Square Adhitya Prayoga Permana; Totok Chamidy; Cahyo Crysdian
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.2023.8.2.112-124

Abstract

Government builds public facilities to support the needs of the community. The use of these public facilities needs to be re-evaluated, and one way to do it is through community response. Google Maps is one platform that receives the most responses from the community about location. Google Maps Reviews allow us to see how the public reacts to a location. Naïve Bayes method is used for classification in this study because it is one of the simple methods in machine learning that can be easily applied to several experiments conducted by the author. In the classification process, reviews produce many features that will be calculated based on their class. More features generated, more features processed too in the system. Chi-Square feature selection will be used to reduce features that have low dependence on the system. In this study, performance values will be calculated based on the experimental use of feature ratios of 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, and 100%. The results show that the use of 10% Chi-Square features produces the best performance, with an accuracy rate of 86.94%, precision of 80.42%, recall of 80.42%, and f-measure of 80.42%.
Analysis of the Use of Artificial Neural Network Models in Predicting Bitcoin Prices Muhammad Sahi; Muhammad Faisal; Yunifa Miftachul Arif; Cahyo Crysdian
Applied Information System and Management (AISM) Vol 6, No 2 (2023): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v6i2.29648

Abstract

Bitcoin is one of the fastest-growing digital currencies or cryptocurrencies in the world. However, the highly volatile Bitcoin price poses a very extreme risk for traders investing in cryptocurrencies, especially Bitcoin. To anticipate these risks, a prediction system is needed to predict the fluctuations in cryptocurrency prices. Artificial Neural Network (ANN) is a relatively new model discovered and can solve many complex problems because the way it works mimics human nerve cells. ANN has the advantage of being able to describe both linear and non-linear models with a fairly wide range. This research aims to determine the best performance and level of accuracy of the ANN model using the Back-Propagation Neural Network (BPNN) algorithm in predicting Bitcoin prices. This study uses Bitcoin price data for the period 2020 to 2023 taken from the CoinDesk market. The results of this study indicate that the ANN model produces the best performance in the form of four input nodes, 12 hidden nodes, and one output node (4-12-1) with an accuracy rate of around 3.0617175%.
KLASIFIKASI LINEARITAS SERAPAN LULUSAN SEKOLAH MENENGAH KEJURUAN MENGGUNAKAN ALGORITMA NAIVE BAYES Putra, Hirga Ertama; Crysdian, Cahyo; Imammudin, M.
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 9, No 2 (2024)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v9i2.5721

Abstract

Pendidikan merupakan instrumen dasar dalam peningkatan kualitas suatu bangsa dan negara. Melalui Sumber Daya Manusia (SDM) yang handal akan menambah kesejahteraan dan peningkatan kualitas bangsa. Secara yuridis SMK dibentuk untuk memberikan alumni yang siap bekerja. Berdasarkan CNN Indonesia menyampaikan bahwa TPT (Tingkat Pengangguran Terbuka) tamatan SMK masih merupakan yang paling tinggi dibandingkan tamatan jenjang pendidikan lainnya. Berbeda dengan kondisi yang ada di sekolah, banyak sekolah yang sudah bisa menyalurkan alumninya dengan cara mengadakan kegiatan jobfair. Dengan menggunakan Machine Learning diharapkan dapat membantu untuk mengklasifikasikan linearitas serapan lulusan siswa SMK. Algoritma Naive Bayes dipilih untuk mengolah dataset serapan lulusan SMK sehingga akan menghasilkan performa accuracy, precision, recall dan f1-score . Dari hasil pengujian algoritma Naive Bayes pada strategi eksperimen menghasilkan nilai accuracy sebesar 92%, precision sebesar 92%, recall sebesar 100% dan f1-score sebesar 96%
PENILAIAN KELAYAKAN CALON PENYEDIA JASA KONSTRUKSI PENGADAAN BARANG/JASA PEMERINTAH MENGGUNAKAN MACHINE LEARNING Yustina, Eva; Amin Hariyadi, Mokhamad; Crysdian, Cahyo
Jurnal Mnemonic Vol 7 No 1 (2024): Mnemonic Vol. 7 No. 1
Publisher : Teknik Informatika, Institut Teknologi Nasional malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/mnemonic.v7i1.7996

Abstract

Pengadaan Barang/Jasa Pemerintah bidang konstruksi menjadi kunci terlaksananya pembangunan infrastruktur pada pemerintah pusat maupun daerah. Dalam menentukan calon penyedia jasa konstruksi dengan metode pengadaan langsung merupakan tugas dari Pejabat Pengadaaan Barang/Jasa. Dengan banyaknya calon penyedia jasa konstruksi yang ada maka perlu dilakukan penilaian kelayakan calon penyedia jasa konstruksi. Penelitian ini menggunakan metode Decision Tree dan Random Forest untuk penilaian calon penyedia jasa konstruksi berupa perseroan terbatas maupun commanditare vennootschap dengan menggunakan dataset terdiri dari 154 record yang terdiri dari 5 variabel antara lain: tenaga ahli, pengalaman kerja, kualitas hasil pekerjaan, menang tender, dan nilai kontrak. Hasil Akurasi metode Random Forest lebih tinggi dibandingkan dengan dengan metode Decision Tree. Metode Random Forest menghasilkan akurasi sebesar 90,91% dengan nilai Area Under Curve (AUC) sebesar 0,471, sedangkan metode Decision Tree menghasilkan akurasi sebesar 84,85%, dengan nilai Area Under Curve (AUC) sebesar 0,693
Determining recipients of uninhabitable house rehabilitation program assistance using the classification method Silfiyah, Chilmiatus; Kusumawati, Ririen; Crysdian, Cahyo
Jurnal Mantik Vol. 8 No. 1 (2024): May: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.v8i1.5184

Abstract

The data used in this study amounted to 15182 datasets consisting of 14 variables. Existing variables are divided into basic variables and additional variables. The basic variables consist of 5 variables namely Home ownership, Roof type, Wall type, Floor type, Defecation facilities. While the additional variables consist of 9 variables, namely employment, having money / livestock / jewelry deposits and others, welfare deciles, education, recipients of non-cash food assistance, recipients of productive assistance for micro enterprises, recipients of cash social assistance, recipients of family hope programs, and recipients of basic necessities. Using the naïve bayes algorithm classification method, the values of accuracy, precision, recall, and f-measure are 67.61%, 67.97%, 93.71% and 78.79%. The addition of additional variables to the basic variables resulted in an accuracy of 68.29% in the additional variables of education. This shows that by adding additional variables, the accuracy results are higher than using only basic variables, so that this study can be used as a recommendation in decision making on the implementation of determining the beneficiaries of the rehabilitation program for uninhabitable houses
The Evaluation of Entropy-based Algorithm towards the Production of Closed-Loop Edge Crysdian, Cahyo
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.7.4.1727

Abstract

This research concerns the common problem of edge detection that produces a disjointed and incomplete edge, leading to the misdetection of visual objects. The entropy-based algorithm can potentially solve this problem by classifying the pixel belonging to which objects in an image. Hence, the paper aims to evaluate the performance of entropy-based algorithm to produce the closed-loop edge representing the formation of object boundary. The research utilizes the concept of Entropy to sense the uncertainty of pixel membership to the existing objects to classify pixels as the edge or object. Six entropy-based algorithms are evaluated, i.e., the optimum Entropy based on Shannon formula, the optimum of relative-entropy based on Kullback-Leibler divergence, the maximum of optimum entropy neighbor, the minimum of optimum relative-entropy neighbor, the thinning of optimum entropy neighbor, and the thinning of optimum relative-entropy neighbor. The experiment is held to compare the developed algorithms against Canny as a benchmark by employing five performance parameters, i.e., the average number of detected objects, the average number of detected edge pixels, the average size of detected objects, the ratio of the number of edge pixel per object, and the average of ten biggest sizes. The experiment shows that the entropy-based algorithms significantly improve the production of closed-loop edges, and the optimum of relative-entropy neighbor based on Kullback-Leibler divergence becomes the most desired approach among others due to the production of more considerable closed-loop edge in the average. This finding suggests that the entropy-based algorithm is the best choice for edge-based segmentation. The effectiveness of Entropy in the segmentation task is addressed for further research. 
Optimizing Goods Placement in Logistics Transportation using Machine Learning Algorithms based on Delivery Data Syawab, Moh Husnus; Arief, Yunifa Miftachul; Nugroho, Fresy; Kusumawati, Ririen; Crysdian, Cahyo; Almais, Agung Teguh Wibowo
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 8 No. 2 (2024)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v8i2.1321

Abstract

This study addresses the challenge of predicting the optimal placement of goods for expeditionary transportation. Efficient placement is crucial to ensure that goods are transported in a manner that maximizes space and minimizes the risk of damage. This study aims to develop a prediction system using the K-Nearest Neighbor (KNN) method, which is based on expert data from expedition vehicles. To evaluate the effectiveness of the KNN method, the researcher compared it with the Support Vector Machine (SVM) method. By doing so, they sought to determine which method delivers more accurate predictions for the optimal placement of goods. The test results revealed that the KNN method outperformed SVM, achieving a higher accuracy of 95.97% compared to SVM's 92.85%. Additionally, KNN demonstrated a lower Root Mean Square Error (RMSE) of 0.18, indicating more precise predictions, while SVM had an RMSE of 0.271. These findings suggest that KNN is the more effective method for predicting the optimal placement of goods in expeditionary transportation.
Early Detection of Phishing Sites with Enhanced Neural Network Models Suarti, Isa; Chamidy, Totok; Crysdian, Cahyo
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 2 (2024): September 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i2.30068

Abstract

Phishing is a digital crime committed with the aim of obtaining personal data by creating a link or website that resembles the original. This form of cyber attack is caused by a notification in a text message, email, or phone call. A common anti-phishing countermeasure technique is to perform early detection of potentially phishing sites, primarily according to the source code features, which are required to traverse web page content, as well as third parties that slow down the process of clarifying phishing URLs. Although the latest technology has long been used in phishing early detection, there is still a need for manual feature engineering that is important and reliable enough to detect emerging phishing offenses. One of these involves training a neural network (NN) using a dataset of known phishing URLs and legitimate URLs. The research was conducted using 200 data, Data were separated into training and testing categories.  Training was done using 100 and 120 data. Training results on 100 data and 160 data had lower iterations and errors on the tanh activation function compared to the logistic activation function. The number of iterations that occur in logistic activation is as many as 400 iterations, while when using the tanh activation function only 175 iterations are needed.
AUTOMATED MULTI-MODEL PREDICTION AND EVALUATION FOR CONNECTING RAINFALL PREDICTION INFORMATION AND SINGLE-YEAR OPERATIONAL PLAN OF LAHOR-SUTAMI DAM Mahmudiah, Rikha Rizki; Crysdian, Cahyo; Hariyadi, Mokhamad Amin; Kurniawan, Andang
EnviroScienteae Vol 20, No 4 (2024): ENVIROSCIENTEAE VOLUME 20 NOMOR 4, NOVEMBER 2024
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/es.v20i4.21054

Abstract

There is a gap between existing climate information and the needs of annual dam operational planning. This study aims to demonstrate that the percentile approach currently used for planning is not optimal, especially now that automation has become more accessible. The purpose of this study is to design an automated forecasting and evaluation system based on 36 10-days rainfall projections using a multi-model approach. This approach comprises a percentile, ARIMA, ECMWF+ARIMA, IOD DMI regression, ERSST regression, and ensemble methods models. Additionally, this study aims to demonstrate how a verified, multi-model-based rainfall forecast can provide more reliable assurance for the annual operational planning of Lahor-SutamiDam, simulated operationally in November 2022 for the 2022/2023 planning cycle. Data utilized include historical 10-days rainfall data from 1991 to 2023, ECMWF raw and corrected model outputs, Nino-Dipole index, and global sea surface temperature. The verification method employs four criteria based on MAE and fit index. An operational simulation approach is used for training-testing period segmentation, while a 10-year window is applied to account for possible climate-change-induced shifts in relationships. Single linear regression is used to avoid overfitting. The automation system was developed using R-Statistics. Results indicate that the current approach is only optimal for 58% of locations. Superior methods identified include ECMWFcorrected, ERSST regression, and Ensemble models. A case study for 2022/2023 demonstrates that the forecast results outperform the existing plan for at least 78% of the projected periods.
Co-Authors Adhitya Prayoga Permana Agung Teguh Wibowo Almais Ajib Hanani Akbar Roihan Arafat, Ihsan Bagus Fahad Arief, Yunifa Miftachul Atmalia, Citra Fidya Aziz, Kevin Elmy Fachri, Ahnaf Febriyan Fatchurrohman Fatchurrohman, Fatchurrohman Fresy Nugroho Galib, Galan Ramadan Harya Ihtada, Fahrendra Khoirul Imammudin, M. Irwan Budi Santoso Johan Ericka Wahyu Prakasa Khadijah Fahmi Hayati Holle Khamidatullailiyah, Yayang Kurniawan, Andang Ma'muri, Ma'muri Mahmudiah, Rikha Rizki Mauludiah, Siska Farizah Mochamad Imamudin Mokhamad Amin Hariyadi Muhammad Faisal Muhammad Faisal Muhammad Sahi Mustaqim, Eka Rifut Nur Okta Qomaruddin Aziz Pangestu, Denis Aji Pratama, Mohammad Yoga Putra, Hirga Ertama Putra, Kurniawan Tri Qosim, Ahmad Latif Ririen Kusumawati Romadhani, Ahmad Roro Inda Melani Rounaqi, Muhammad Fairuz Zumar S, Puspa Miladin Sahi, Muhammad Setiawan, Dwi Risky Silfiyah, Chilmiatus Suarti, Isa Sugiharto, Tomy Ivan Susilo, Ni`mah Firsta Cahya Sya'roni Syahreni, Syahreni Syawab, Moh Husnus Totok Chamidy Tri Gunantohadi Usman Pagalay Yuliawan, Audi Bayu Yunifa Miftachul Arif Yustina, Eva Zainal Abidin Zamzam, Muhammad Adib Zarkoni, Ahmad