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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) BERKALA FISIKA Media Statistika Jurnal Masyarakat Informatika MATEMATIKA JURNAL SISTEM INFORMASI BISNIS Semantik Jurnal Ilmiah Kursor Jurnal Inovasi Teknologi Pendidikan Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) Register: Jurnal Ilmiah Teknologi Sistem Informasi Proceeding of the Electrical Engineering Computer Science and Informatics AKSIOMA Jurnal Teknologi dan Sistem Komputer Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Matematika: MANTIK Jurnal Penelitian Pendidikan IPA (JPPIPA) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control BAREKENG: Jurnal Ilmu Matematika dan Terapan Unisda Journal of Mathematics and Computer Science (UJMC) JTAM (Jurnal Teori dan Aplikasi Matematika) Jurnal Sisfokom (Sistem Informasi dan Komputer) Journal of Applied Data Sciences Journal of Soft Computing Exploration Interdisciplinary Social Studies Journal of Fundamental Mathematics and Applications (JFMA) Buletin Abdi Masyarakat (BAM) Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya Scientific Journal of Informatics
Bayu Surarso
Department Of Mathematics & Graduate School Of Information System
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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Sistem Pakar Untuk Identifikasi Dan Alternatif Solusi Terhadap Permasalahan Yang Dihadapi Peserta Didik Sekolah Menengah Menggunakan Rule-Based Machine Learning Syafei, Wahyul Amien; Sulistiyo, Budi; Surarso, Bayu
Jurnal Teknologi dan Sistem Komputer [IN PRESS] Volume 10, Issue 3, Year 2022 (July 2022)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jtsiskom.2022.13896

Abstract

Kesulitan dalam mengidentifikasi masalah yang dialami oleh peserta didik di sekolah tingkat menengah berpotensi mengakibatkan masalah yang lebih besar di kemudian hari. Selama ini guru bimbingan dan konseling (BK) menggunakan metode konvensional dalam mengidentifikasi dan memecahkan masalah tersebut. Metode ini membutuhkan biaya yang besar, ruang yang khusus, dan waktu yang lama. Artikel ini memaparkan pengembangan sistem pakar untuk identifikasi untuk kemudian menawarkan alternatif solusi terhadap permasalahan yang dihadapi peserta didik di tingkat sekolah menengah. Sistem ini menggunakan metode Problem Checklist yang didukung oleh machine learning untuk meningkatkan akurasi dan efisiensi. Kepakaran dari guru BK senior untuk menghubungkan ragam permasalahan dan berbagai alternatif solusi dilatihkan pada rule-based machine learning sistem ini. Pengujian dilakukan menggunakan WEKA dengan 200 contoh data sampel sebagai data pelatihan, yang dapat memprediksi data dari 185 contoh yang label kelasnya tidak diketahui Sistem yang dikembangkan adalah berbasis web sehingga peserta didik yang merasa mengalami masalah dapat meng-ases dirinya sendiri dan mendapatkan saran alternatif solusi secara online. Guru BK sebagai admin dapat memantau perkembangan peserta didiknya serta melakukan penambahan ragam permasalahan dan alternatif solusi mengikuti perkembangan jaman.  Hasil implementasi dan pengujian menunjukkan bahwa sistem pakar yang dikembangkan menawarkan identifikasi dan solusi yang akurat dan lebih cepat serta dapat dilakukan kapanpun dan di manapun.
Gaussian filter-based dark channel prior for image dehazing enhancement Nurhayati, Oky Dwi; Surarso, Bayu; Syafei, Wahyul Amien; Nugraheni, Dinar Mutiara Kusumo
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5765-5778

Abstract

The presence of haze in an image is one of the challenges in computer vision tasks, such as remote sensing, object monitoring, and traffic monitoring applications. The hazy image is considered to contain noise and it can interfere with the image analysis process. Thus, image dehazing becomes a necessity as part of image enhancement. Dark channel prior (DCP) is one of the images dehazing methods that works based on a physical degradation model and utilizes low-intensity values from outdoor image characteristics. The DCP method generally consists of some steps, which are finding the dark channel and gradient image, estimating the sky region, atmospherical light, and transmission map, and reconstructing the dehazed image. This study introduces image dehazing by utilizing the Gaussian filter combined with the DCP method to increase the sharpness and accentuate the details of hazy images. Experimental results show that the proposed method could produce dehazed images with a visual quality is 18.94 dB on average or an increase of 11.91% compared to the original hazy image with a similarity index is 66.71% on average or an increase of 8.10%. Therefore, it is expected that this study can contribute to the image dehazing method enrichment based on DCP.
Pembelajaran Daring Berbasis Youtube dan Open Broadcasting System Bagi Guru-guru MA Muallimin Muallimat Rembang Oky Dwi Nurhayati; Bayu Surarso; Migunani Migunani; Ahmad Aviv Mahmudi
Buletin Abdi Masyarakat Vol 4, No 2 (2024): Edisi Februari 2024
Publisher : Universitas YPPI Rembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47686/bam.v4i2.670

Abstract

The development of Information and Communication Technology (ICT) as a learning medium requires teachers to have adequate knowledge and skills in integrating it into each subject so that the quality of learning can be improved. One of the phenomena currently visible is that teachers have not been able to use ICT properly as a learning medium. This is caused by many factors, including lack of knowledge and skills, wrong views about ICT. Madrasah Aliyah Mu'allimin Mu'allimat Rembang (M3R) is located on the outskirts of Rembang city, precisely in Kabongan Kidul Village, Rembang District, Rembang Regency. The number of students actively studying is 360 students, taught by 25 teachers and the support of 6 educational staff. . M3R as an institution is also required to provide the latest innovations to form an effective learning process. However, not all teachers and educational staff do not understand the latest innovations that must be used to carry out learning by utilizing internet media which can be utilized optimally. Due to the lack of teachers' ability to create content or learning materials and classroom teaching online, it is necessary to increase the capacity and skills of teachers in creating content and managing online teaching. The method used in the PkM program provides training and practice as well as assistance in the use of information and communication technology in learning activities at schools using open broadcasting systems and YouTube. The outcome of this PkM program is that teachers master and are able to create and use learning media using open broadcasting systems and YouTube and implement them as learning media.Keywords: Information and Communication Technology, open broadcasting system, YouTube, content, learning
The forecasting of palm oil based on fuzzy time series-two factor Wulandari, Ratri; Surarso, Bayu; Irawanto, Bambang; Farikhin, Farikhin
Journal of Soft Computing Exploration Vol. 2 No. 1 (2021): March 2021
Publisher : SHM Publisher

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

Abstract

Palm oil is a vegetable oil obtained from the mesocarp fruit of the palm tree, generally, from the species, Elaeis guineensis, and slightly from the species Elaeis oleifera and Attalea maripa. Palm oil is naturally red due to its high alpha and beta-carotenoid content. Palm kernel oil is different from palm kernel oil produced from the same fruit core. Planning for palm oil production is necessary because it greatly affects to the level of the country’s economy. Forecasting can reduce uncertainty in planning. Forecasting used in the palm oil problem is two-factor forecasting using the Kumar method with uama factors in the form of palm oil production and supporting factors in the form of land area. The forecasting is evaluated using AFER and MSE, from the acquisition of AFER value of 1.212% <10%, then the forecasting has very good criteria.
Car insurance segmentation prediction based on the most influential features using random forest and stacking ensemble learning Vianita, Etna; Wibowo, Adi; Surarso, Bayu; Widodo, Aris Puji
Journal of Soft Computing Exploration Vol. 2 No. 2 (2021): September 2021
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v2i2.39

Abstract

In addition to financial transaction services, the Bank also provides insurance services by conducting regular campaigns to attract new customers such as car insurance based on market segmentation, which is one of the main aspects of marketing used in financial services based on demographic data. One way to analyze the market is to predict the likely target market based on the campaign's target demographic data. Therefore, this study aims to find the best classification method for predicting campaign targets using historical data from 4000 customers of a bank in the United States. The market segmentation analysis process uses the best feature selection and ensemble learning. The best feature selection is selected using important features for Random Forest. The ensemble learning used is a stacking model consisting of the basic model of Logistic Regression, Support Vector Classifier, Gradient Boosting, Extra Tree, Bagging, Adaboost, Gaussian Naive Bayes, MLP, XBoost, LGBM, KNeighbors, Decision Tree, and Random Forest. The accuracy results of the stacking model can exceed the accuracy of the basic model with an accuracy rate of 78.80%.
Pendekatan Momen untuk Metode Magnitude pada Bilangan Trapezoidal Fuzzy Aulia, Lathifatul; Irawanto, Bambang; Surarso, Bayu
Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya 2018: Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1575.509 KB)

Abstract

Teori himpunan fuzzy banyak diterapkan dalam berbagai disiplin ilmu. Para ahli telah banyak yang mengusulkan beberapa pendekatan untuk memecahkan masalah yang menggunakan himpunan bilangan fuzzy. Hal utama yang perlu dilakukan dalam menyelesaikan suatu permasalahan yang menggunakan bilangan fuzzy yaitu defuzzifikasi. Defuzzifikasi merupakan proses mentransformasikan bilangan fuzzy menjadi bilangan riil tegas atau disebut dengan penegasan bilangan fuzzy. Ada beberapa metode yang dapat digunakan untuk menegaskan suatu bilangan fuzzy. Setiap metode penegasan bilangan fuzzy yang berbeda akan menghasilkan bilangan tegas (crisp) yang berbeda pula. Pada tulisan ini, dibahas metode Magnitude yaitu merupakan metode pendekatan yang ditunjukkan dengan perhitungan momen daerah rata-rata yang mempertimbangkan fungsi keanggotaan bilangan fuzzy, penyebaran fungsi kenggotaan kanan, dan fungsi keanggotaan kiri pada beberapa potongan –
Traditional-Enhance-Mobile-Ubiquitous-Smart: Model Innovation in Higher Education Learning Style Classification Using Multidimensional and Machine Learning Methods Santiko, Irfan; Soeprobowati, Tri Retnaningsih; Surarso, Bayu; Tahyudin, Imam; Hasibuan, Zainal Arifin; Che Pee, Ahmad Naim
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.598

Abstract

Learning achievement is undoubtedly impacted by each person's unique learning style. The assessment pattern is less focused due to the intricacy of the current components. In fact, general elements like VARK are thought to create complexity that can impair focus when combined with elements like environmental conditions, teacher effectiveness, and stakeholder policies. Although it is only ideal in specific areas, the application of supported information technology has so far yielded positive results. This essay attempts to be creative in evaluating how well students learn in higher education settings. An assessment framework that uses multidimensionality and simplifies features is the innovation that is being offered. Method, Material, and Media (3M) are the three categories into which simplification of aspects is separated. However, the Dimensions are categorized into five groups: Traditional, Enhance, Mobile, Ubiquitous, and Smart (TEMUS). Approximately 1200 respondents consisting of students and lecturers formed into a dataset in 2 types of data, namely test data and training data. The trial was conducted using 4 models, namely Random Forest, SVM, Decision Tree, and K-Nearest. The test results were interpreted in MSE, R-Square, Accuracy, Recall, Precision, and F1-Score. Based on the comparison of test results, it states that Random Forest has the most optimal results with MSE values of 0.46, R Square 0.99, Accuracy 0.86, Recall 0.86, Precision 0.87, F1 Score 0.84. Based on the results obtained, it proves that in addition to being able to carry out the classification process, the TEMUS Dimensional Framework can form a pattern of compatibility with each other, between the learning styles of Lecturers and Students. According to this TEMUS framework, teacher and student performance will be deemed suitable and effective when the 3M components are assessed from both perspectives in the same way. If not, a review will be conducted.
Implementasi E-Commerce dengan Sistem Informasi Rekomendasi menggunakan Metode Collaborative Filtering untuk Pengembangan Penjualan pada UMKM Khusnah, Miftakhul; Gernowo, Rahmat; Surarso, Bayu
Jurnal Sistem Informasi Bisnis Vol 15, No 1 (2025): Volume 15 Number 1 Year 2025
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/vol15iss1pp134-141

Abstract

MSMEs are one of the micro businesses that are carried out to improve the prosperity of home industry, the majority of MSMEs still carry out traditional business processes, but in the current era, product sales can be done anywhere, such as running an online business through e-commerce. The ease of this online business helps MSMEs to develop sales globally,  so e-commerce is needed which will be aquipped with a recommendation information system for sales development in MSMEs. This research aims to implement a recommendation information system in e-commerce using the collaborative filtering method. This method was chosen because of its advantages in producing more accurate recommendations using MSME data, consumer data, and rating data. From the process carried out, the results show that this system provides product recommendations with the highest predictive value, namely M1 is product RSM with a predictive value of  0,5. M3 is product RPC with a predictive value of  0,03. M4 is product RKK with a predictive value of  1. M6 is product RKC with a predictive value of  0,88 which will be displayed to consumers and provide an effective and efficient marketing platform.
Measuring interest and talent in determining learning using the quadrant model in the learning process in a smart classroom Santiko, Irfan; Soeprobowati, Tri Retnaningsih; Surarso, Bayu; Tahyudin, Imam
Jurnal Inovasi Teknologi Pendidikan Vol. 12 No. 1 (2025): March
Publisher : Universitas Negeri Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/jitp.v12i1.73585

Abstract

Naturally, the learning process in smart classrooms is greatly impacted by the trend of individual learning, now known as personalized learning. Several studies have demonstrated that, because of the problem of technological advances, the effectiveness of the anticipated results has not been fully achieved. While there are technology benefits, some scholars link them to issues. This study aims to demonstrate it by evaluating learning interests and talents. A sample of at least 1000 students from 419 universities participated in the questionnaire experiment. Each of the three questionnaire domains, affective, cognitive, and psychomotor, was examined using ANOVA. The coefficient test uses two variables: interest and talent. With an ANOVA P-value of 0.021 for psychomotor and 0.031 for affective and cognitive, the three domains demonstrated a statistically significant connection. The coefficients of interest and talent, which average between 1 and 0.05 for P emotional and cognitive interest (0.054) and P talent (0.023) and between 0.027 and 0.055 for P psychomotor interest and P talent, demonstrate the significant values of both factors. The developed interest and talent measuring model can be used to forecast learning outcomes based on these findings. In addition to information technology, the results of this interest and talent-measuring design can be utilized to define and evaluate the learning process, including its appropriateness. Further research recommendations include a framework to measure interests and talents early, aiding admissions, curriculum, resources, methods, and learning media development.
STOCK PRICE FORECASTING USING FUZZY C-MEANS AND TYPE-2 FUZZY TIME SERIES Satriani, Rineka Brylian Akbar; Farikhin, Farikhin; Surarso, Bayu
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp1365-1378

Abstract

Stock prices have unstable movements, so forecasting is needed to decide to invest appropriately according to the strategy. Fuzzy Time Series (FTS) uses fuzzy sets to forecast future time series values using historical data. However, interval partitioning in FTS needs to be considered as it can affect the forecasting results. FCM is applied to solve the problem of interval assignment in the universe of discourse. It allows the evaluation of the distribution of historical data and forming intervals of different sizes. Type 2 Fuzzy Time Series (T2FTS) is an extension of FTS to improve forecasting performance and refine fuzzy relationships. This research aims to improve forecasting accuracy using the Fuzzy C-Means (FCM)-T2FTS combination. This research uses daily data on BBRI stock prices from January 2023 to May 2024, with the variables used being close, high, and low prices. The results showed that determining the interval length using unequal length is more efficient than fixed interval length and can improve model performance, demonstrated from the MAPE values of T2FTS and FCM-T2FTS, which are 2.09% and 1.97%, respectively, the difference between the two MAPEs, is 0.12%. Hence, FCM-T2FTS is 12% more efficient than T2FTS. Therefore, FCM-T2FTS can improve forecasting accuracy.
Co-Authors A. Nafis Haikal Adi Wibowo Adi Wibowo Agus Subagio Ahmad Abdul Chamid Ahmad Aviv Mahmudi Aldi Setiawan, Aldi Alfajri, Willy Bima Ali Bardadi Anak Agung Gede Sugianthara Andi Setiabudi, Nur Antariksa, Muhammad Deagama Surya Arief Hidayat Aris Puji Widodo Aris Sugiharto Aslam Fatkhudin Aulia, Lathifatul Badieah Assegaf Bambang Irawanto Beta Noranita Budi Warsito Budi Warsito Budi Warsito Che Pee, Ahmad Naim Dedy Kurniadi Dinar Mutiara Kusumo Nugraheni Dwi Putri Handayani Dwiyanasari, Desty Edwin Setiawan Eko Adi Sarwoko Eko Sediyono Etna Vianita Fajar Nugraha Fra Siskus Dian Arianto Ghufron Ghufron Harjito - Henny Indriyawati Imam Tahyudin Indah Jumawanti Irfan Santiko I’tishom Al Khoiry Jatmiko Endro Suseno Jumawanti, Indah Jumawanti, Indah Juwanda, Farikhin Khoerunnisa, Selvi Fitria Khusnah, Miftakhul Laily Rahmania, Laily Lili Rusdiana, Lili LM Fajar Israwan, LM Fajar Lucia Ratnasari Masruroh, Fitriana Maunah, Uun Migunani Migunani Muhammad Haris Qamaruzzaman Muhammad Nasrullah Muhammad Sam&#039;an Mustafid Mustafid Mustaqim Mustaqim Mustaqim Mustaqim, Mustaqim Nugraheni, Dinar Oky Dwi Nurhayati Pukky Tetralian Bantining Ngastiti Puspita, Yuanita Candra Putri, Aina Latifa Riyana Putri, Nitami Lestari Putut Sriwasito Rachmat Gernowo Ragil Saputra Ragil Saputra Rahmat Gernowo Rahmawati, Nurhita Ratri Wulandari Rezki Kurniati, Rezki Robertus Heri Sulistyo Utomo Saputra, Ragil Satriani, Rineka Brylian Akbar Siti Alfiatur Rohmaniah St. Budi Waluya Sugiyamto Sugiyamto, Sugiyamto Sulastri Daruni Sulistiyo, Budi Suryono Suryono Suryono Suryono Suryono, Suryono Susi Hendartie Susilo Hariyanto sutimin sutimin Sutrisno, Sutrisno Sutrisno, Sutrisno Syibli, Mohammad T Indriastuti . Titi Udjiani SRRM Tri Retnaningsih Soeprobowati Uswatun Khasanah Vianita, Etna Wahyul Amien Syafei Wicaksono, Mahad Zainal Arifin Hasibuan