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All Journal Register: Jurnal Ilmiah Teknologi Sistem Informasi JURNAL KESEHATAN LINGKUNGAN: Jurnal dan Aplikasi Teknik Kesehatan Lingkungan IJIE (Indonesian Journal of Informatics Education) The Spirit of Society Journal : International Journal of Society Development and Engagement SINTECH (Science and Information Technology) Journal Jurnal Informatika Universitas Pamulang Journal of Information Technology and Computer Engineering Journal of Informatics and Vocational Education Jurnal PkM (Pengabdian kepada Masyarakat) JUSTIN (Jurnal Sistem dan Teknologi Informasi)
Febri Liantoni
Universitas Sebelas Maret
Arts Humanities Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Public Health Social Sciences Other

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PERBAIKAN KONTRAS CITRA MAMMOGRAM PADA KLASIFIKASI KANKER PAYUDARA BERDASARKAN FITUR GRAY-LEVEL CO-OCCURRENCE MATRIX Febri Liantoni; Agus Santoso
SINTECH (Science and Information Technology) Journal Vol. 3 No. 1 (2020): SINTECH Journal Edition April 2020
Publisher : LPPM STMIK STIKOM Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31598/sintechjournal.v3i1.528

Abstract

In this era to recognize breast tumors can be based on mammogram images. This method will expedite the process of recognition and classification of breast cancer. This research was conducted classification techniques of breast cancer using mammogram images. The proposed model targets classification studies for cases of malignant, and benign cancer. The research consisted of five main stages, preprocessing, histogram equalization, convolution, feature extraction, and classification. For preprocessing cropping the image using region of interest (ROI), for convolution, median filter and histogram equalization are used to improve image quality. Feature extraction using Gray-Level Co-Occurrence Matrix (GLCM) with 5 features, entropy, correlation, contrast, homogeneity, and variance. The final step is the classification using Radial Basis Function Neural Network (RBFNN) and Support Vector Machine (SVM). Based on the hypotheses that have been tested and discussed, the accuracy for RBFNN is 86.27%, while the accuracy for SVM is 84.31%. This shows that the RBFNN method is better than SVM in distinguishing types of breast cancer. These results prove the process of improving image construction using histogram equalization and the median filter is useful in the classification process.
Analisis Implementasi Teknologi Cloud Computing pada Universitas Ditinjau dari Total Cost of Ownership Muhammad Jaelani; Puspanda Hatta; Febri Liantoni
Journal of Informatics and Vocational Education Vol 4, No 3 (2021): Journal of Informatics and Vocational Education
Publisher : Pendidikan Teknik Informatika dan Komputer, Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/joive.v4i3.55132

Abstract

Penelitian ini bertujuan untuk mengetahui perhitungan pembiayaan infrastruktur cloud computing ditinjau dari segi Total Cost of Ownership. Metode dalam penelitian ini menggunakan metode penelitian kuantitatif dengan desain penelitian Cross Sectional, yaitu desain penelitian yang menekankan pada waktu pengukuran atau observasi data yang dilakukan pada variabel terikat maupun variabel bebas dalam satu kali pada satu waktu. Pengumpulan data dilakukan dengan melakukan wawancara dan observasi. Objek pada penelitian ini, yaitu keseluruhan device yang digunakan pada infrastruktur cloud computing dan non-cloud yang terdapat di ICT FKIP UNS dan UPT TIK UNS. Hasil penelitian yang diperoleh menunjukkan ICT FKIP UNS dan UPTI TIK UNS mengeluarkan biaya lebih sedikit pada infrastruktur cloud computing dan biaya lebih besar pada infrastruktur non-cloud. Dan pengeluaran untuk infrastruktur cloud computing lebih murah dari pengeluaran untuk infrastruktur non-cloud, baik pada perhitungan dengan amortisasi maupun tanpa amortisasi. Dari hasil penelitian tersebut dapat disimpulkan bahwa perhitungan TCO pada infrastruktur cloud computing dan non-cloud menunjukkan biaya yang dikeluarkan untuk infrastruktur cloud computing lebih murah daripada infrastruktur non-cloud, baik pada ICT FKIP UNS maupun UPT TIK UNS. Serta penggunaan amortisasi pada perhitungan model TCO tidak memberikan pengaruh terhadap hasil akhir perbandingan perhitungan karena baik dengan amortisasi maupun tanpa amortisasi, biaya untuk infrastruktur cloud computing tetap lebih murah daripada infrastruktur non-cloud.
Prediksi Penambahan Kasus Covid-19 di Indonesia Melalui Pendekatan Time Series Menggunakan Metode Exponential Smoothing Calvin Mikhailouzna Gibran; Sulis Setiyawati; Febri Liantoni
Jurnal Informatika Universitas Pamulang Vol 6, No 1 (2021): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/informatika.v6i1.9442

Abstract

The Covid-19 pandemic in Indonesia has emerged starting in 2020. To know the development of cases, a good calculation is needed. A prediction system can help in analyzing accurate data on positive causes, cures, and deaths. The right prediction or forecast can be the answer to the question of the impact that will occur, forecasting will provide an overview to the government and the community so that it is hoped that related parties can prepare for future impacts or even reduce the number of cases growth. In this study, the Exponential Smoothing method was used as a prediction calculation. This method is simple but effective in producing accurate predictions. Forecasting data used comes from the Indonesian government with the assumption that the data is valid and reliable. Based on research that has been carried out to predict the increase in new cases of the Indonesian National Covid-19, the best alpha (α) value is 0.33 with an SSE of 1048027,939. This shows that the number of cases is increasing. The results of forecasting in this study using the time series approach and the SES method are more suitable for predicting the percentage increase in cases than knowing the exact number.
Desain Smart Body Vest Untuk Meminimalisir Kecelakaan Kerja Menuju Indonesia Zero Accident Mohammad Iskandar Nur Fahmi; Muhammad Bagus Panuntun; Andayani Yuwana Sari; Febri Liantoni
JURNAL KESEHATAN LINGKUNGAN: Jurnal dan Aplikasi Teknik Kesehatan Lingkungan Vol 17, No 2 (2020): Jurnal Kesehatan Lingkungan Volume 17 No. 2, Juli 2020
Publisher : Poltekkes Kemenkes Banjarmasin Jurusan Kesehatan Lingkungan Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (388.754 KB) | DOI: 10.31964/jkl.v17i2.217

Abstract

The construction sector plays an important role in a country's economy. This is because construction projects such as the construction of buildings, roads, bridges and other infrastructure are one of the benchmarks of economic progress and civilization of a country. Work accidents on construction projects can cause work to stop and result in financial losses and decreased work productivity. According to the Minister of Manpower in 2018 the number of work accidents has increased from the previous year even from the data of the Central Statistics Agency stating that the majority of construction workers are junior high school graduates and below. This is one of the factors causing the increase in occupational accidents in the construction sector. Losses from work accidents are also included in workers' losses, damage to equipment and materials wasted due to work accidents. Occupational health and safety (K3) risk control is very important as a preventive effort to prevent a bigger event. One such control is the use of Personal Protective Equipment (PPE) or more commonly called personal protective equipment (PPE). The existence of PPE is important for workers to minimize the impact of accidents so that each company is obliged to use PPE. This study aims to minimize the number of work accidents in Indonesia, especially in the construction sector. The method used in making the body vest is the addition of an airbag by applying the fall detection algorithm to the K-Nearest Neighbor (KNN), which is to calculate the Euclidean distance which is the distance between the sample and training data and then determine the k nearest data from the sample so that the sample can be classified on the sensor and microcontroller. The way it works is when a collision or a hard collision occurs, workers will generally be thrown or dropped then there will be a change in the acceleration of the position of the body wearing a body vest. The change in acceleration triggers the development of airbags on the body vest. This is expected to reduce injuries to vital organs in the worker's body.
Increased Mammogram Image Contrast Using Histogram Equalization And Gaussian In The Classification Of Breast Cancer Febri Liantoni; Coana Sukmagautama; Risalina Myrtha
JITCE (Journal of Information Technology and Computer Engineering) Vol 4 No 01 (2020): Journal of Information Technology and Computer Engineering
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (613.675 KB) | DOI: 10.25077/jitce.4.01.40-44.2020

Abstract

Breast cancer is one of the most common diseases among women in several countries. One of the most common methods to diagnose breast cancer is mammography. In this study, we propose a classification study to differentiate benign and malignant breast tumors based on mammogram image. The proposed system includes five major steps, i.e. preprocessing, histogram equalization, convolution, feature extraction, and classification. Image is cropped using region of interest (ROI) at preprocessing stage. In this study, we perform image contrast quality enhancement of the mammogram to view the breast cancer better. Image contrast enhancement uses histogram equalization and Gaussian filter. Gray-Level Co-Occurrence Matrix (GLCM) is used to extract the mammogram features. There are five features used i.e. entropy, correlation, contrast, homogeneity, and variance. The last step is to classify using naïve Bayes classifier (NBC) and k-nearest neighbor (KNN). Based on the hypothesis, the accuracy of NBC method is 90% and the accuracy of KKN method is 87.5%. So, the mammogram image contrast enhancement is well performed.
Workshop And Motivation For Improving Student Skills Through The Information And Communications Technology Febri Liantoni; Yusfia Hafid Aristyagama; Nurcahya Pradana Taufik Prakisya; Puspanda Hatta; Cucuk Wawan Budiyanto
THE SPIRIT OF SOCIETY JOURNAL : International Journal of Society Development and Engagement Vol 5 No 1 (2021): September 2021
Publisher : LPPM of NAROTAMA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29138/scj.v5i1.1431

Abstract

In the digital age, the role of information technology is needed to face competition in the community. Information and communication technology is an important element in contributing to changes that are fundamental to the structure of operations and management of organizations, education, transportation, health, and research. The internet is like two sides of a coin, the content offered is positive and negative, both are very dependent on the behavior of its users. The ease of access to the internet is increasingly being felt by the public with increasingly cheap hardware such as tablets and laptops as well as wider connection support. Various efforts to stem negative information continue to be pursued by various elements of society, but it is not effective if the user behavior is not changed. Teenagers are among the most vulnerable in the misuse of advances in internet technology, so it needs serious efforts to provide the right knowledge and skills in utilizing these advancements. By conducting workshops and motivation to improve the abilities and skills of Girimarto 1 High School students, it is hoped that school students can face the development of the digital era more readily. The results of this training gained a high level of satisfaction with the material that had been carried out.
The Implementation of Decision Tree Classification Techniques to Predict the Duration of Students Completing the Thesis at PTIK FKIP UNS Halim Perdana Kesuma; Dwi Maryono; Febri Liantoni
Journal of Informatics and Vocational Education Vol 5, No 2 (2022): Journal of Informatics and Vocational Education - July
Publisher : Pendidikan Teknik Informatika dan Komputer, Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/joive.v5i2.64790

Abstract

This paper aims to determine the reasons why students take a long time in compiling their thesis. The slowness of students in compiling will have an impact on their graduation. This is a serious problem faced by educational institutions. Out of 328 students at PTIK FKIP UNS who took thesis credits, only 85 were able to graduate on time. Therefore, this study was conducted to identify the causes. The research data was taken from the alumnus class of 2012 to 2017. The data was processed using RapidMiner software. The technique used was the decision tree classification technique with the C4.5 algorithm, and to optimize the accuracy of the model, the Particle Swarm Optimization (PSO) algorithm was also added. This study got an accuracy rate of 76% and an AUC score of 0.733.
Penerapan Cooperative Learning Pada Pembelajaran Daring Mata Pelajaran Informatika Ditinjau Dari Keaktifan dan Prestasi Belajar Siswa di SMP. Rifa'i Abdul Karim; Dwi Maryono; Febri Liantoni
Journal of Informatics and Vocational Education Vol 5, No 2 (2022): Journal of Informatics and Vocational Education - July
Publisher : Pendidikan Teknik Informatika dan Komputer, Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/joive.v5i2.62546

Abstract

This study aims to increase student activity and learning achievement through the application of Cooperative Learning Type STAD in Informatics online learning by utilizing the Google Jamboard application. This research is included in the quasi-experimental type of research with the design of One Group Pretest-Posttest.  The population in this study was all grade VII students of a state owned school. The sample used was class VII G students with a total of 32 students. The sampling technique uses cluster random sampling. Data collection techniques use questionnaires and tests. The technologyk data analysis used is a normality test, homogeneity test, and hypothesis test using a paired sample t test. The results of the study, which were reviewed in terms of learning activity, statistically there was an increase from an average score  of 72.90 to 80.69. Meanwhile, in terms of learning achievement, it showsthat statistically the average pre-test score is46.56 and  the average  post test score is 65.03 so that there is an increase through the application of Cooperative Learning Type STAD in informatics online learning.
Effect of information gain on document classification using k-nearest neighbor Rifki Indra Perwira; Bambang Yuwono; Risya Ines Putri Siswoyo; Febri Liantoni; Hidayatulah Himawan
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol. 8 No. 1 (2022): January
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v8i1.2397

Abstract

State universities have a library as a facility to support students’ education and science, which contains various books, journals, and final assignments. An intelligent system for classifying documents is needed to ease library visitors in higher education as a form of service to students. The documents that are in the library are generally the result of research. Various complaints related to the imbalance of data texts and categories based on irrelevant document titles and words that have the ambiguity of meaning when searching for documents are the main reasons for the need for a classification system. This research uses k-Nearest Neighbor (k-NN) to categorize documents based on study interests with information gain features selection to handle unbalanced data and cosine similarity to measure the distance between test and training data. Based on the results of tests conducted with 276 training data, the highest results using the information gain selection feature using 80% training data and 20% test data produce an accuracy of 87.5% with a parameter value of k=5. The highest accuracy results of 92.9% are achieved without information gain feature selection, with the proportion of training data of 90% and 10% test data and parameters k=5, 7, and 9. This paper concludes that without information gain feature selection, the system has better accuracy than using the feature selection because every word in the document title is considered to have an essential role in forming the classification.
The Influence of the Learning Cycle Blended Learning Model on Student Learning Outcomes Pratiwi Ajeng Safitri; Basori Basori; Febri Liantoni
Journal of Informatics and Vocational Education Vol 6, No 3 (2023): Journal of Informatics and Vocational Education - November
Publisher : Pendidikan Teknik Informatika dan Komputer, Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/joive.v6i3.76753

Abstract

Learning in the 21st century requires students to be literate about the existence of technology. Technology users in Indonesia are increasing, but the quality of Indonesian education internationally, based on UNESCO records, is still far behind other ASEAN countries. COVID-19, once endemic in Indonesia, directly impacted education, which had to be carried out online. However, after Covid-19 subsided, learning was resumed face-to-face as usual. The Blended Learning learning model, suitable for the 21st century, should not be discontinued but should be improved and improved to make it more optimal. This research aims to determine the effect of the Learning Cycle Blended Learning model on student learning outcomes with the help of Moodle. This research uses quantitative methods with a quasi-experimental research design. This research involved 71 students in the experimental class with the Learning Cycle Blended Learning model and the control class with the Problem-Based Learning learning model. The instrument used is a learning outcomes test consisting of a pretest and a posttest. The t-test results show (1) differences in student learning outcomes between applying Problem-Based Learning and the 8E-Blended Learning cycle with a Sig value. (2-tailed) is 0.003. The n gain test results show (1) The 8E-Blended Learning cycle learning model is more effective in improving student learning outcomes with a gain score of 49.46 in the good category.
Co-Authors Agus Santoso Andayani Yuwana Sari Bambang Yuwono Basori Basori Calvin Mikhailouzna Gibran Coana Sukmagautama Cucuk Wawan Budiyanto Dini Erlinawati Dwi Maryono Endar Suprih Wihidayat Erica Devi Ayuningrum Fadil Indra Sanjaya Halim Perdana Kesuma Hidayatulah Himawan Jose Feliciano Lim Belo Ximenes Meiyanto Eko Sulistyo Mohammad Iskandar Nur Fahmi Muhammad Bagus Panuntun Muhammad Hamka Ibrahim Muhammad Jaelani Mulia Sulistiyono Nurcahya Pradana Taufik Prakisya Pratiwi Ajeng Safitri Puspanda Hatta Rifa'i Abdul Karim Rifki Indra Perwira Risalina Myrtha Risya Ines Putri Siswoyo Subuh Pramono Sulis Setiyawati Sutrisno Sutrisno Yuliana Rizki Ikhsanty Yusfia Hafid Aristyagama