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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Pendidikan Teknologi dan Kejuruan Teknika Jurnal Simetris Bulletin of Electrical Engineering and Informatics Jurnal Ilmu Komputer dan Agri-Informatika Proceeding International Conference on Information Technology and Business Journal of Information Systems Engineering and Business Intelligence KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Jurnal Komputasi InComTech: Jurnal Telekomunikasi dan Komputer Jurnal Sains dan Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING PROSIDING KOMUNIKASI Jurnal Teknoinfo Jurnal Teknologi Komputer dan Sistem Informasi Building of Informatics, Technology and Science JUTIS : Jurnal Teknik Informatika Indonesian Journal of Electrical Engineering and Computer Science Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Informasi dan Sains Teknologi Buguh: Jurnal Pengabdian Kepada Masyarakat Jurnal Pepadun Jurnal Siger Matematika AMMA : Jurnal Pengabdian Masyarakat SULUH ABDI : Jurnal Ilmiah Pengabdian Kepada Masyarakat JTKSI (Jurnal Teknologi Komputer dan Sistem Informasi)
Kurnia Muludi
Jurusan Ilmu Komputer FMIPA Universitas Lampung
Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Nursing Social Sciences Other

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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

Abstract

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 TEKNOLOGI PETA VIRTUAL 3D GEDUNG E TEKNIK SIPIL DAN GEDUNG F LABORATORIUM HIDROLIKA FAKUTAS TEKNIK UNIVERSITAS LAMPUNG Aristoteles Aristoteles; Maya Asterita; Yunda Heningtyas; Kurnia Muludi; Admi Syarif
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.v3i1.95

Abstract

Lampung University doing out routine activities every new academic year for students especially for new students college who majoring in Civil Engineering, Faculty of Engineering, Introduction about Campus Life for New Students (PKKMB), one of the main agendas of PKKMB is introducing about everyvbuildings and public facilities in Civil Engineering also every building and the room in it. The current state of the COVID-19 pandemic make the University of Lampung conduct learning activy by network (online). So, it's make every students activities not possible to perform direct tracing. Serving method information follows technological developments, one of which is 3D visualization techniques. With using the Multimedia Development Life Cycle (MDLC) system development method, 3D virtual map applications can be developed that can display building layouts and facilities in 3D. This research is Alpha Testing and Customer satisfaction. Alpha Testing provides test results The application can operate on versions in operation android system 7.1 to 10. The application can operate on smartphone with screen specifications from 5.0 inch to 6.5 inches. The application can operate on smartphones from minimum 3GB RAM to 8GB RAM. Customer Satisfaction Respondents stated that the 3D Virtual Map Application of Building E Civil Engineering and Building F of the Hydraulics Laboratory get good results with a percentage index between 87.5% to 95%.
Bilangan Kromatik Lokasi Subdivisi Operasi Barbel Tertentu Graf Origami \mathbit{B}_{\mathbit{O}_\mathbf{3}}^\mathbit{s}, \mathbit{B}_{\mathbit{O}_\mathbf{4}}^\mathbit{s}, \mathbit{B}_{\mathbit{O}_\mathbf{5}}^\mathbit{s}, \mathbit{B}_{\mathbit{O}_\mathbf Agus Iriawan; Asmiati Asmiati; La Zakaria; Kurnia Muludi; Bernadhita Herindri Samodra Utami
Jurnal Siger Matematika Vol 2, No 2 (2021): Jurnal Siger Matematika
Publisher : FMIPA Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (273.73 KB) | DOI: 10.23960/jsm.v2i2.2932

Abstract

Let G=(V,E) be a connected graph and c be a proper k-coloring of G with color 1,2,...,k. Let {C_1,C_2,...,C_k} be a partition of V(G) which is induced by coloring c. The color code c_Phi(v) of v is the ordered k-tuple (d(v,C_1),d(v,C_2),...,d(v,C_k)) where  d(v,C_i)= min{d(v,x)|x \in C_i}  for any i. If all distinct vertices of G have distinct color codes, then c is called  k-locating coloring of G. The locating-chromatic number, denoted by \chi_L(G), is the smallest k such that G has a locating k-coloring.  Subdivision certain barbell origami graphs, for s>=1, is a graph with  V\left(B_{O_n}^s\right)=\left\{u_i,u_{n+i},v_i,v_{n+i},w_i,w_{n+i}\middle|1\le i\le n\right\} U {x_i|1<=i<=s}  and E\left(B_{O_n}^s\right)={{u}_iw_i,u_iv_i,v_iw_i,u_iu_{i+1},w_iu_{i+1}|1\le i\le n} U {{u}_{n+i}w_{n+i},u_{n+i}v_{n+i},v_{n+i}w_{n+i},u_{n+i}u_{n+i+1},w_{n+i}u_{n+i+1}|1\le i\le n-1} U {u_nx_1,x_nu_{n+1}} U{x_ix_{i+1}|1\le i\le s-1}. In this paper, we will determined the locating-chromatic number of subdivision certain barbell origami graphs B_{O_3}^s,B_{O_4}^s , B_{O_5}^s and  B_{O_6}^s.
Winner-Takes-All based Multi-Strategy Learning for Information Extraction Dwi Hendratmo Widyantoro; Kurnia Muludi; Kuspriyanto Kuspriyanto
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 11: November 2014
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i11.pp7935-7945

Abstract

This paper proposes a winner-takes-all based multi-strategy learning for information extraction. Unlike the majority of multi-strategy approaches that commonly combine the prediction of all base learnings involved, our approach takes a different strategy by employing only the best, single predictor for a specific information task. The best predictor (among other predictors) is identified during training phase using k-fold cross validation, which is then retrained on the full training set. Empirical evaluation on two benchmarks data sets demonstrates the effectiveness of our strategy. Out of 26 information extraction cases, our strategy outperforms other information extraction algorithms and strategies in 16 cases. The winner-takes-all strategy in general eliminates the difficult situation in multi-strategy learning when the majority of base learners cannot make correct prediction, resulting in incorrect prediction on its output. In such a case, the best predictor with correct prediction  in our strategy will take over for the overal prediction.
PENDAMPINGAN PENGEMBANGAN E-COMMERCE DI KWTH KARTINI, DESA KUBU BATU, KABUPATEN PESAWARAN PADA ERA DIGITALISASI DALAM PENINGKATAN EKONOMI MASYARAKAT Rahmat Safei; Aristoteles Aristoteles; Rahmat Safe'i; Admi Syarif; Kurnia Muludi; Favorisen R. Lumbanraja
BUGUH: JURNAL PENGABDIAN KEPADA MASYARAKAT Vol. 2 No. 3 (2022)
Publisher : Badan Pelaksana Kuliah Kerja Nyata Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (399.547 KB) | DOI: 10.23960/buguh.v2n3.1173

Abstract

Adanya kelompok atau komunitas merupakan keuntungan bagi masyarakat yang dapat dapat mendukung kesejahteraan dan perkembagan keberlangusngan hutan rakyat. Hal tersebut dapat diperkuat lagi dan didukung dengan adanya pemanfatan dan implementasi dari adanya teknologi pada era digitalisasi ini. Namun masih terdapat kendala dalam pelaksanaannya, seperti masih rendahnya pengetahuan masyarakat, masih kurangnya arus untuk mendapatkan informasi, dan masih kurangnya peran eksternal dalam membangun kapasitas masyarakat. Sasaran mitra dalam kegiatan pengabdian ini yaitu Kelompok Wanita Tani Hutan (KWTH) Kartini Desa Kubu Batu, Kabupaten Pesawaran. Pengabdian ini bertujuan untuk meningkatkan pengetahuan, keterampilan, dan kemampuan mitra dalam proses implementasi tekonologi. Untuk mencapai hal tersebut maka rangkaian kegiatan pengabdian ini antara lain: tahap persiapan, meliputi: sosialisasi kegiatan pengabdian berisi program dan tujuan pengabdian serta manfaat dan sasaran. Untuk mengukur pengetahuan peserta dilakukan evaluasi pre-test dan post-test dengan cara pemberian kuesioner. Hasil menunjukkan bahwa adanya peningkatan pengetahuan peserta tentang materi memanfaatkan dan mengelola website. Rerata nilai pre-test dan post-test masing-masing sebesar 50,3%. dan 75,6%. Adanya kenaikan pengetahuan peserta dari hasil pre-test dan post-test adalah 25,3. Oleh karena itu, pemanfaatan dan pengelolaan website merupakan media yang tepat untuk mempromosikan produk yang ditawarkan oleh KWTH Kartini.
Comparative Analysis between Rabin Karp Algorithm, Winnowing, and Turnitin Applications for Detecting Plagiarized Words Ari Kurniawan Saputra; Kurnia Muludi; Taqwan Thamrin
Prosiding International conference on Information Technology and Business (ICITB) 2020: INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND BUSINESS (ICITB) 6
Publisher : Proceeding International Conference on Information Technology and Business

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

Abstract

Plagiarism was considered to violate the law because it copied of other people's works. To prevent plagiarism in terms of scientific writing, every word or sentence of scientific texts had to be detected through word-matching algorithm in the written text. The objective of this study was to compare the accuracy of values between the Rabin Karp Algorithm and the Winnowing Algorithm. The accuracy of values of these algorithms was compared with that of the values of Turnitin application. The data analysis used in this study was Hypothesis T-test. The result of this study was that the value of Rabin Karp Algorithm was higher than that of the Winnowing Algorithm and the Turnitin Application.Keywords: Plagiarisme, Turnitin, Rabin Karp Algorithm, Winnowing Algorithm
Comparison of Data Mining Classification Methods for Predicting Credit Appropriation through Naïve Bayes and Decision Tree Methods Rendi Irawan; Agustinus Eko Setiawan; Kurnia Muludi
Prosiding International conference on Information Technology and Business (ICITB) 2020: INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND BUSINESS (ICITB) 6
Publisher : Proceeding International Conference on Information Technology and Business

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

Abstract

The problem statement of this study was seen on inaccurate assessment of the debtors’ ability in paying off the loan of their businesses so that it often caused credit problems. Data Mining was used in assessing or predicting creditworthiness for a prospective debtor. The author attempted to compare the data mining classification to analyze the credit feasibility prediction through Naïve Bayes and Decision Tree methods. The data of the prospective debtors had been processed through the stages of data mining – Naïve Bayes and Decision Tree. The data were tested through k-folds cross-validation (k = 10). The result of this study was that the accuracy of the method of Decision Tree (J-48) was higher than that of the method of Naïve Bayes. The result of the comparison of the two algorithms was that the Decision Tree (J-48) algorithm had an accuracy of 95.24% and the Naïve Bayes algorithm had an accuracy of 79, 59%.Keywords: Credit, Naïve Bayes, Decision Tree, K-Folds Validation
Penerapan Metode Support Vector Machine (SVM) dalam Klasifikasi Penderita Diabetes Mellitus Fanni Lufiana; Favorisen Rosyking Lumbanraja; Yunda Heningtyas; Kurnia Muludi
Jurnal Pepadun Vol. 4 No. 1 (2023): 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.v4i1.150

Abstract

Diabetes Mellitus (DM) is a chronic disease characterized by the body's inability to metabolize carbohydrates, fats, and proteins, resulting in increased blood sugar (hyperglycemia) due to low insulin levels. Diabetes is due to a combination of heredity (genetics) and unhealthy lifestyles. Hemoglobin A1c is a blood test used to diagnose and manage diabetes patients when measuring blood sugar levels. This study aims to analyze predictive models for the classification of people with diabetes using R Shiny and evaluate the results of the support vector machine method's classification performance. There are many ways to diagnose diabetes, and the support vector machine is one of the machine learning algorithms used in this study's classification case (SVM). This study uses data from Diabetes 130-US Hospital For Years 1999-2008, which was sourced from the UCI Machine Learning Repository and consists of 34 variables and 84900 records, with dataset distribution and testing techniques using the 10-fold cross-validation method and three kernels in modeling using SVM, namely linear, Gaussian, and polynomial. The results obtained are a simple predictive model analysis system for classifying people with diabetes with shiny, making it easier for users to find out the prediction results and obtain the highest accuracy result, which is 82.76 percent of the gaussian kernel.
Sistem Pakar Kasus dan Aturan pada Hukum Kesehatan Indonesia Berbasis Android Admi Syarif; Noverina Rahmaniyanti; Yulia K. Wardani; Kurnia Muludi
KLIK- KUMPULAN JURNAL ILMU KOMPUTER Vol 10, No 2 (2023)
Publisher : Lambung Mangkurat University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/klik.v10i2.545

Abstract

The doctor-patient relationship is not always harmonious. Not only that, but patients also often have problems with other health workers such as midwives, nurses, and others. Health disputes are caused by patient disappointment and dissatisfaction with the services and medical treatment received before, during, or after treatment This study aims to build an expert system for identifying health dispute cases and related rules Android-based. This system adopts an  Android-based forward chaining method. The system is tested using 5 actors and 35 cases. The results obtained are compared with the results given by the experts. The test results show that the system is effective in explaining the rules based on the case.
Implementasi Metode CNN Computer Vision Dalam Identifikasi Tipe Kerusakan Pohon Berbasis FHM Rahmat Safe’i; Rahmat Safe&#039;i; Zuhri Nopriyanto; Rico Andrian; Kurnia Muludi
InComTech : Jurnal Telekomunikasi dan Komputer Vol 13, No 1 (2023)
Publisher : Department of Electrical Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/incomtech.v13i1.16022

Abstract

Identifikasi tipe kerusakan pohon pada Forest Health Monitoring hingga saat ini masih bersifat manual, yaitu menggunakan penglihatan manusia dalam penerapannya. Teknologi Informasi yang kini berkembang pesat dapat di rasakan hingga ke berbagai media penerapan ilmu pengetahuan, dengan demikian terciptalah salah satu solusi dalam memecahkan masalah penelitian kasus identifikasi tipe kerusakan pohon yaitu dengan penggunaan metode computer vision dalam upaya memudahkan pekerjaan dalam ilmu kehutanan. Tujuan penelitian ini adalah untuk menerapkan computer vision dalam mengidentifikasi tipe kerusakan pohon berbasis Forest Health Monitoring. Tahapan penelitian yang dilakukan dalam penelitian ini adalah pengumpulan dataset, proses preprocessing, pembagian dataset, pelatihan model, pengujian model dan terakhir adalah evaluasi model. Hasil penelitian ini berupa model (prototype) identifikasi tipe kerusakan pohon dalam 4 kategori yaitu, LeNet-5 Colab, LeNet-5 Tesla, MobileNet Colab, dan MobileNet Tesla. Persentase identifikasi model bervariasi, dimana pada kelas tertentu model dapat mengidentifikasi dengan benar dan dikelas lainnya masih ada beberapa identifikasi model yang kurang optimal, disebabkan oleh kemiripan beberapa bentuk dataset dalam segi visual komputer. Penelitian penerapan computer vision dalam identifikasi kerusakan pohon berbasis Forest Health Monitoring berhasil dilakukan dengan menghasilkan dua model (prototype) dalam identifikasi kerusakan pohon yang nilai akurasinya mencapai angka 89.99% pada model LeNet-5 dan 99.06% pada model MobileNet dengan tools yang digunakan adalah Jupyter notebook pada Nvidia Tesla K20 (offline) dan Google Colab (online).
Co-Authors ., Rusliyawati Admi Syarif Aflaha Asri Agus Iriawan Agus Wantoro Agus Wantoro Agus, Isnandar Agustinus Eko Setiawan Ahmad Habibullaah Ahyarudin Akbar, Mohammed Raihan Akmal Junaidi Alfabet Setiawan Alfi, Firmansyah Yuni Andika Yuda Andreas Perdana Andri Winata Andrian, Rico Anggun Falianingrum Apri Candra Widyawati Aprilia, Indri Mada Ari Kurniawan Saputra Aristoteles, Aristoteles Asmiati Asmiati Assidik, Reksa Qodri Astria Hijriani Astria Hijriani Astria Hijriani Astria Hijriani, Astria Aulia Putri Ariqa Bayu Ade Candra, Bayu Ade Bernadhita Herindri S. Utami Budiman Ruliansyah candra, bayu ade dedi kurniawan Dian Kurniasari Didik Kurniawan Dimas Aminudin Saputra Djauharie, Arlyandi S Djuadi, Noverman Dwi Hendratmo Widyantoro Dwi Sakethi Eko Priyanto Erlina Ain Andini Eva Diana Sari Evita Fitriasari Fajri Reskanida Fanni Lufiana Fanni Lufiana Fathur Rahmi Febi Eka Febriansyah Febi Eka Febriansyah Heni Sulistiani Heningtyas, Yunda Herlina Herlina Ida Nurhaida Irawati, Anie Rose Jayawarsa, A.A. Ketut Jihan Aferiansyah Kenny Claudie Fandau Khairun Nisa Khalida Zhia Kurnia Muludi KUSPRIYANTO La Zakaria Lia Atika Rani Lumbanraja, Favorisen R M Said Hasibuan Machudor Yusman Machudor Yusman Mahfut Mardiana Mardiana Maya Asterita Meizari, Ary Mohammad Surya Akbar Muhammad Apriansyah Setiawan Muhammad Iqbal Muhammad Irfan Ardiansyah Muharni, Sita Ni Putu Ayu Anesca Noni Kurniasih Noverina Rahmaniyanti Okta Viana Ossy Dwi Endah Wulansari Pratama, Rinaldo Adi Rahmat Safe'i Rangga Firdaus Rangga Firdaus Rendi Irawan Resti Lucyana Reza Aji Saputra Riska Aprilia Romadhoni, Nuzul Rahmat SAIFUL ANWAR Saur Pangihutan Sinurat Saur Pangihutan Sinurat, Saur Pangihutan Shofiana, Dewi Asiah Singagerda, Faurani Santi Siti Maesaroh Sonianto Sonianto Sonianto Sonianto Sri Ratna Sulistiyanti Sri Wahyuningsih Sutyarso Sutyarso Syangap Diningrat Sitompul Tantut Wahyu Setyoko Taqwan Thamrin Tia Ayu Muliana Tiyara Saghira Triloka, Joko Tristiyanto Tristiyanto Tundjung Tripeni Handayani Warsito Warsito Wartariyus Wartariyus Yugo Prasojo, Diaji Yulia K. Wardani Yuni Rahayu Yuni Rahayu, Yuni Zuhri Nopriyanto Zuriana Zuriana