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All Journal International Journal of Electrical and Computer Engineering TEKNIK INFORMATIKA Jurnal Simetris Bulletin of Electrical Engineering and Informatics Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) Jurnal Pengabdian Kepada Masyarakat Sakai Sambayan WARTA KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Science and Technology Indonesia JOIV : International Journal on Informatics Visualization Jurnal Komputasi Jurnal Teknoinfo Jurnal Tekno Kompak Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Pengabdian Kepada Masyarakat (JPKM) Tabikpun Buguh: Jurnal Pengabdian Kepada Masyarakat International Journal of Electronics and Communications Systems Jurnal Pepadun Journal of Data Science and Software Engineering Warta LPM Laporan Upaya Nyata Inovasi Ilmu Komputer (LUNIK) Integra: Journal of Integrated Mathematics and Computer Science

Lumbanraja, Favorisen R

Jurusan Ilmu Komputer, Fakultas Matematika Dan Ilmu Pengetahuan Alam, Universitas Lampung

Author-ID : 3250375

Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Physics Social Sciences Other

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KLASIFIKASI ABSTRAK JURNAL KOMPUTASI MENGGUNAKAN METODE TEXT MINING DAN ALGORITMA SUPPORT VECTOR MACHINE Eliza Fitri; Favorisen R. Lumbanraja; Ardiansyah Ardiansyah
Jurnal Pepadun Vol. 1 No. 1 (2020): December
Publisher : Department of Computer Science, Faculty of Mathematics and Natural Sciences, University of Lampung

The University of Lampung especially Computer Science Departement has an online journal that publishes various scientific articles written by researchers both students and lecturers. This scientific article is called the online Computating Journal which is published once every 6 months. But, this online Computating Journal has not been structured and classified into the category of science that more specific. Therefore, in this research the abstract Computating Journal will be classified using text mining techniques to process the abstract become more structured and retrieve information in it. Then, the information in the abstract is extracted as a feature by the TFIDF weighting technique. The proposed classification model uses the support vector machine algorithm that has strong consistency. The model classification will be validated by applying the 10-Fold Cross Validation technique.

IDENTIFIKASI KAIN TAPIS LAMPUNG MENGGUNAKAN EKSTRAKSI FITUR EDGE DETECTION (CANNY) DAN KLASIFIKASI PROBABILITY NEURAL NETWORK (PNN) Admi Syarif; M. Juandhika Rizky; Rico Andrian; Favorisen R. Lumbanraja
Jurnal Pepadun Vol. 2 No. 1 (2021): April
Publisher : Department of Computer Science, Faculty of Mathematics and Natural Sciences, University of Lampung

Tapis fabric is a Lampung tribe women's clothing in the form of a sarong made of woven cotton threads with a sugi motif or decoration, silver thread or gold thread with an embroidery system. Lampung tapis is the result of woven cotton threads with motifs, silver threads or gold threads and becomes the typical clothing of the Lampung tribe. Tapis fabric can be distinguished by shape and pattern, each type has its own special characteristics. This study aims to identify the tapis fabric using Edge Detection feature extraction and Probability Neural Network (PNN) classification. Experiments were carried out using 525 images data, 450 images became training data, while the other 75 images became test data consisting of 3 types of filters Bintang Perak, Gunung Beradu, and Sasab. The results of the experiment are quite good. The smoothing value applied to the PNN has an effect on the accuracy.

KLASIFIKASI KEJADIAN HIPERTENSI DENGAN METODE SUPPORT VECTOR MACHINE (SVM) MENGGUNAKAN DATA PUSKESMAS DI KOTA BANDAR LAMPUNG Indah Pasaribu; Favorisen Rosyking Lumbanraja; Dewi Asiah Shofiana; Aristoteles Aristoteles
Jurnal Pepadun Vol. 2 No. 2 (2021): August
Publisher : Department of Computer Science, Faculty of Mathematics and Natural Sciences, University of Lampung

Hypertension is a condition in which a person experiences an increase in blood pressure above the normal value which causes pain and even death. Normal human blood pressure is 120/80 mmHg. Patients with hypertension cannot be cured, but prevention and control can be done. The hypertension cases are always increasing in Indonesia. The Bandar Lampung City Health Service stated that hypertension is a disease that always ranks in the top ten diseases in Bandar Lampung City. Diagnosis of hypertension is currently manually performed by requiring a lot of energy, materials, and time. Based on the condition, there is an idea to apply the field of biomedical data analysis to help diagnosing hypertension using the support vector machine (SVM) method in Bandar Lampung City. This study classifies and measures the accuracy of the support vector machine method in hypertension. The data comes from five health centers in Bandar Lampung City from 2017 to 2019 with 10-fold cross validation data sharing. The kernels used are linear, gaussian, and polynomial kernels. This study successfully classifies hypertension sufferers in Bandar Lampung City. The result of the highest feature correlation analysis is 0.90. The results of the classification using the support vector machine method get the highest accuracy, which is 99.78% on the gaussian kernel.

CLUSTERING K-MEANS JENIS KATA PADA LAPORAN KEGIATAN KULIAH KERJA NYATA (KKN) UNIVERSITAS LAMPUNG MENGGUNAKAN WORD2VEC Kristina Ademariana; Aristoteles Aristoteles; Favorisen Rosyking Lumbanraja; Rico Andrian
Jurnal Pepadun Vol. 2 No. 2 (2021): August
Publisher : Department of Computer Science, Faculty of Mathematics and Natural Sciences, University of Lampung

Kuliah Kerja Nyata (KKN) is a form of student service activities for the community, requesting and developing science and technology carried out off-campus within a period, linking work, and special requirements managed by the Badan Pelaksana Kuliah Kerja Nyata (BP-KKN). While carrying out KKN activities, each group of students is required to upload a report of the activities carried out in the village. In uploading the report file, there are several categories in each activity, including socialization, training, and character development. To classify the results of uploading activities one of which can be done using clustering techniques. In this research, a clustering of discussion on KKN student activities will be conducted at the University of Lampung. The text mining method is used to process KKN student activities to be more structured. Information on the KKN student activities was obtained as a feature with the Word2Vec weighting technique. The algorithm used is the K-Mean algorithm which has a high accuracy of the size of the object, so this algorithm is relatively more measurable and efficient for processing large numbers of objects. From the results of research conducted, it has been found that apply the text mining process algorithm for clustering with the K-means method on the Unila KKN Student activity data produces a value of k = 2, a lot of filtered data in the preprocess is 6284 data, using this method has not yet gotten a good association analysis because the results of the second cluster do not show the general types of words, typos and reporting activities by students who are not specifically can affect the results of clustering that is not good.

IMPLEMENTASI SUPPORT VECTOR MACHINE DALAM MEMPREDIKSI HARGA RUMAH PADA PERUMAHAN DI KOTA BANDAR LAMPUNG Favorisen Rossyking Lumbanraja; Reza Aji Saputra; Kurnia Muludi; Astria Hijriani; Akmal Junaidi
Jurnal Pepadun Vol. 2 No. 3 (2021): December
Publisher : Department of Computer Science, Faculty of Mathematics and Natural Sciences, University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/pepadun.v2i3.90

Machine Learning has been widely used in terms of predictions for analyzing datasets. One method of Machine Learning is Support Vector Machine (SVM). The house has an important role in the survival of human beings. With the times, many developers are competing to build housing. The purpose of this study is to predicted the housing cost using Support Vector Machine. The data in this research used the data of house in Bandar lampung, the price, the location and the building specification. The amount of data used 51 datas and 33 variables with regression and classification, also used 3 kernels and it's model, 12 times first trial and next 6 experiments done with fitur selection. The trial result was kernel regression polynomial model reached the highest R 2 that was 95,99% linear kernel and gaussian kernel reached R 2 90,99% and 81,43% each. While in accuration classification model trial is obtained in 8 class of gaussian kernel as big as 91,18%, and linear kernel and polynimonal kernel get an accuracy of 90,20% and 89,90%.

IMPLEMENTASI ALGORITME SUPPORT VECTOR MACHINE DAN FITUR SELEKSI MRMR UNTUK PREDIKSI GLIKOSILASI Favorisen Rosyking Lumbanraja; Naurah Nazhifah; Dewi Asiah Shofiana; Akmal Junaidi
Jurnal Pepadun Vol. 3 No. 1 (2022): April
Publisher : Department of Computer Science, Faculty of Mathematics and Natural Sciences, University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/pepadun.v3i1.96

During the protein formation process, there are post-translational modifications that provide additional properties and produce new R groups in the polypeptide chain. One of the results of post-translational modifications is glycosylation. Glycosylation reactions occur between protein and glucose at high concentrations. There are 3 categories of glycosylation found in the human body, namely N-glycosylation, O-glycosylation and C-glycosylation. To determine the functional role of glycosylation, that is by predicting the substrate of each glycosylation site. A computational approach is a way to predict the glycosylation site, using the Support Vector Machine (SVM) algorithm. In this study there are 2 types of data, namely independent data and benchmark data. The features used are feature extraction and feature selection using Maximum Redundancy Minimum Relevance (MRMR) of 25, 50 and 75 columns. SVM classification test using 5-fold cross validation. The highest accuracy result lies in the use of the 75 column MRMR selection feature. In Independent N data, the greatest accuracy lies in the sigmoid kernel with a causation value of 86.66%, while for independent C data, the accuracy is 87.5% in the sigmoid kernel and for independent O data, the largest accuracy is 89.31% which is in the RBF kernel. For benchmark N data, the highest accuracy is 70.54% in the RBF kernel, for benchmark C data the greatest accuracy lies in the RBF kernel with a value of 95.06% and for benchmak O data it is in the RBF kernel with the greatest accuracy, which is 92.64%.

PENGEMBANGAN SISTEM INFORMASI ADMINISTRASI ASISTEN MATA KULIAH PRAKTIKUM DAN RESPONSI DI JURUSAN ILMU KOMPUTER UNIVERSITAS LAMPUNG Nova Ayu Lestari Siahaan; Akmal Junaidi; Favorisen Rosyking Lumbanraja; Bambang Hermanto
Jurnal Pepadun Vol. 3 No. 2 (2022): August
Publisher : Department of Computer Science, Faculty of Mathematics and Natural Sciences, University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/pepadun.v3i2.116

The processing system of the administrative information for lab works and problem exercises of the student at the Department of Computer Science, Universitas Lampung, is still carried out conventionally. In this manner, the data is physically stored in the form of recorded documents and leave them on the shelf. Sometimes, data collection in this form can be lost and require a longer time to manage. For this reason, a lab assistant’s information system is developed by the waterfall approach that runs on a web-based platform. The system can provide data and generate information about lab assistants and participants faster and more accurately. It is implemented using the framework Laravel and Black Box testing as a functional test of the system. The testing indicates that the system functions work to the software requirement specification.

IMPLEMENTASI SUPPORT VECTOR MACHINE (SVM) DALAM MEMPREDIKSI JUMLAH PENYAKIT DEMAM BERDARAH (STUDI KASUS PENYEBARAN DEMAM BERDARAH DI SINGAPURA) Danu Sasmita; Favorisen Rosyking Lumbanraja; Kurnia Muludi; Astria Hijriani
Jurnal Pepadun Vol. 3 No. 2 (2022): August
Publisher : Department of Computer Science, Faculty of Mathematics and Natural Sciences, University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/pepadun.v3i2.123

Dengue Hemorrhagic Fever (DHF) is an infectious disease caused by dengue virus infection and is transmitted through the bite of female mosquito species Aedes aegypti and Aedes albopictus. Environmental factors are one of the causes of the high prevalence of dengue fever, including the layout of buildings, water reservoirs, indentations in the soil, temperature and other things that can help the Aedes mosquito life cycle take place. The purpose of this study is to predict the spread of dengue disease using the SVM (Support Vector Machine) method with rainfall data in Singapore from 2014 to 2018, weather data and pain data, comparing this study with previous research by Adeline Ong in 2014 entitled " Predicting Dengue Cases in Singapore”, as well as knowing the results of predicting the distribution of DHF using the SVM method in the form of variance values (R2) with linear, gaussian and polynomial kernels. The results of the experiment found that the lowest error value was shown by the linear kernel with an error rate of 35.15%, with a variance value of 64.85%.

Peringkasan Teks Artikel Ilmiah Berbahasa Indonesia dengan Metode Pembobotan Kalimat Desti Fatmalasari; Favorisen Rosyking Lumbanraja
Jurnal Pepadun Vol. 3 No. 3 (2022): December
Publisher : Department of Computer Science, Faculty of Mathematics and Natural Sciences, University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/pepadun.v3i3.127

Technology of document monitoring is used to save time in digging up important information on documents. Summarize is a process of shrinking text shorter but still retaining the information contained therein. This research discusses the commemoration of the text of journal scientific using sentence weighting methods in the form of TF-IDF and Similarity. The goal the system wants to achieve can be to summarize text by recognizing patterns on text documents in txt format files. The system was built using PHP as a programming language. The trial was conducted using UAT (User Acceptance Testing) to find out the response to the interpreted system, namely by the likers scale questionnaire by dividing 3 aspects of the assessment. From the results of data processing (quantitative) obtained a value of 82.6% for the appearance of the system, a value of 80.2% for the efficiency of sentences generated in the system summary, and a value of 83.7% for satisfaction in using an automatic texting system in scientific articles Indonesian. The results of testing and implementation of the automatic text alerting system are received with a relatively strong acceptance rate.

Sistem Informasi Berbasis Web untuk Pengelolaan Unit Jalan Rel dan Jembatan di PT Kereta Api (Persero) Divre IV Tanjung Karang Barat Rangga Agustiantino; Akmal Junaidi; Yohana Tri Utami; Favorisen Rosyking Lumbanraja
Jurnal Pepadun Vol. 3 No. 3 (2022): December
Publisher : Department of Computer Science, Faculty of Mathematics and Natural Sciences, University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/pepadun.v3i3.132

PT Kereta Api Indonesia (Persero) is a company engaged in land transportation. PT KAI employees work in units, one of which is the JJ unit in charge of recording and checking roads and railways. PT KAI (Persero) has implemented an e-Office Information System to facilitate employees' work, but the system does not fully support the JJ Unit. As a result, the JJ Unit has problems where there is a lack of time efficiency when recording work tools, staffing structures, and tracking data that still use Microsoft Excel. One solution to this problem was created: a web-based Rail Road and Bridge Unit Information System at PT KAI (Persero) Divre IV Tanjung Karang. The purpose of this research is to facilitate data searches and data updates needed in the JJ Unit. The stages of this research are data collection, system design, system development, and system testing. The result of this research is the information system of the Rail Road and Bridge Unit at PT KAI (Persero) Divre IV Tanjung Karang based on the Web. This information system was developed with PHP programming, supported by MySQL as the database. The Balsamiq MockUp application was used to design the system interface in the design phase. The system has been tested with BlackBox testing and is supported by results that are as expected. This information system has four levels of users: Senior Manager, Junior Manager, Head Office Staff, and users in each resort and station. Thus, it can be concluded that the Website-based Rail Road and Bridge Unit Information System has been successfully developed using the Waterfall method and using the PHP programming language assisted by MySQL as a database.

Co-Authors - Damayanti Adawiyah, Laila Admi Syarif Aflaha Asri Ahyarudin Akbar, Mohammed Raihan Akmal Junaidi Amelia Jasmine Andrian, Rico Annisa Rizqiana Ardiansyah Ardiansyah Aristoteles, Aristoteles Asmiati Asmiati Astria Hijriani Astria Hijriani Aulia Putri Ariqa Ayu Amalia Bambang Hermanto Damayanti Damayanti Danu Sasmita Desti Fatmalasari Destian ade anggi Sukma Dian Kurniasari Didik Kurniawan Dwi Kartini, Dwi Dwi Sakethi Dwi Sakethi, Dwi Eliza Fitri Elly Lestari Rusitati Erdi Suroso Fanni Lufiana Fanni Lufiana Febi Eka Febriansyah Fitriyana, Silfia Hadi, Normi Abdul Hamim Sudarsono . Hdiana, Yazid Zinedine Heningtyas, Yunda Ilman, Igit Sabda Indah Pasaribu Ira Hariati Br Sitepu Irawati, Anie Rose Jasmine, Amelia Jihan Aferiansyah Junaidi Junaidi Junaidi Junaidi Kristina Ademariana Kurnia Muludi Kurnia Muludi Kurnia Muludi Lilies Handayani M. Juandhika Rizky Machudor Yusman Manurung, Yunita Rosalina Megawaty, Dyah Ayu Meria Nensi Muhammad Reza Faisal, Muhammad Reza Muhammad Rizki Muhaqiqin, Muhaqiqin Muliadi Mustofa Usman Nadila Rizqi Muttaqina Naurah Nazhifah Nirwana Hendrastuty Nova Ayu Lestari Siahaan Nugroho Susanto, Gregorius Nuning Nurcahyani Nurdin, Muhaymi Nurhasanah Nurhasanah Parabi, M. Iqbal Prabowo, Rizky Pratama, Rinaldo Adi Priyambodo Priyambodo Priyambodo Priyambodo Qory Aprilarita Rahmat Safe'i Rangga Agustiantino Reza Aji Saputra RM Sulaiman Sani Rosdiana, Siti Rudy Herteno Rudy Herteno Rusitati, Elly Lestari Saragih, Triando Hamonangan Shofiana, Dewi Asiah Sholehurrohman, Ridho Sintiya Paramitha Siti Aisyah Solechah Siti Rosdiana Su'admaji, Arif Susanto, Gregorius Nugroho Sutyarso Sutyarso Sutyarso, - Syangap Diningrat Sitompul TANJUNG, AKBAR RISMAWAN Tiyara Saghira Tristiyanto Tristiyanto Wamiliana Warsono Warsono Warsono Warsono Warsono YOHANA TRI UTAMI, YOHANA TRI Zuliana Nurfadlilah

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