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All Journal International Journal of Electrical and Computer Engineering TEKNIK INFORMATIKA Jurnal Simetris 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 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
Favorisen R. Lumbanraja
Jurusan Ilmu Komputer, Fakultas Matematika Dan Ilmu Pengetahuan Alam, Universitas Lampung
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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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.
Pendampingan Implementasi Sistem Administrasi Desa untuk Mewujudkan Smart Village di Pekon Wonodadi Kabupaten Pringsewu Lampung Didik Kurniawan; Anie Rose Irawati; Dwi Sakethi; Favorisen Rosyking Lumbanraja
Warta LPM WARTA LPM, Vol. 25, No. 2, April 2022
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1443.082 KB) | DOI: 10.23917/warta.v25i2.647

Abstract

Conceptually, smart villages are related to resource management carried out by the government by utilizing appropriate technology. There are 3 main elements that make up a smart village that must be accommodated properly, namely government, community and environment. Pekon Wonodadi, Gadingrejo District is one of the leading villages in Pringsewu district, Lampung Province and has good potential in the economic field and has carried out village administration well. This condition makes it possible to transform Wonodadi into a Smart Village so that it can improve village functions and services for the community. One of the obstacles faced by Pekon Wonodadi becoming Smart Village is that the public service administration process is still not integrated into a system, so the services provided are still not optimal in terms of speed and accuracy of data. In addition, there is also a problem in the service file archiving process. In order to make Wonodadi becoming a smart village, mentoring activities and implementation of the Village Service Administration System (SIAP) were carried out where these activities correspond to the 2 elements of smart village, namely the government and the community. Evaluation of the activities was carried out by assessing the level of user acceptance of the SIAP application and it was found that the technology implemented could assist Wonodadi Village in improving the quality of service to the community and in the village administration process. This result is reflected in the survey results where 84% of the people feel that it is easier for them to get village services.
LSTM-CNN Hybrid Model Performance Improvement with BioWordVec for Biomedical Report Big Data Classification Kurniasari, Dian; Warsono; Usman, Mustofa; Lumbanraja, Favorisen Rosyking; Wamiliana
Science and Technology Indonesia Vol. 9 No. 2 (2024): April
Publisher : Research Center of Inorganic Materials and Coordination Complexes, FMIPA Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/sti.2024.9.2.273-283

Abstract

The rise in mortality rates due to leukemia has fueled the swift expansion of publications concerning the disease. The increase in publications has dramatically affected the enhancement of biomedical literature, further complicating the manual extraction of pertinent material on leukemia. Text classification is an approach used to retrieve pertinent and top-notch information from the biomedical literature. This research suggests employing an LSTM-CNN hybrid model to tackle imbalanced data classification in a dataset of PubMed abstracts centred on leukemia. Random Undersampling and Random Oversampling techniques are merged to tackle the data imbalance problem. The classification model’s performance is improved by utilizing a pre trained word embedding created explicitly for the biomedical domain, BioWordVec. Model evaluation indicates that hybrid resampling techniques with domain-specific pre-trained word embeddings can enhance model performance in classification tasks, achieving accuracy, precision, recall, and f1-score of 99.55%, 99%, 100%, and 99%, respectively. The results suggest that this research could be an alternative technique to help obtain information about leukemia.
Comparative analysis of deep Siamese models for medical reports text similarity Kurniasari, Dian; Usman, Mustofa; Warsono, Warsono; Lumbanraja, Favorisen Rosyking
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6969-6980

Abstract

Even though medical reports have been digitized, they are generally text data and have not been used optimally. Extracting information from these reports is challenging due to their high volume and unstructured nature. Analyzing the extraction of relevant and high-quality information can be achieved by measuring semantic textual similarity (STS). Consequently, the primary aim of this study is to develop and evaluate the performance of four models: Siamese Manhattan convolution neural network (CNN), Siamese Manhattan long short-term memory (LSTM), Siamese Manhattan hybrid CNN-LSTM, and Siamese Manhattan hybrid LSTM-CNN, in determining STS between sentence pairs in medical reports. Performance comparisons were conducted using Cosine Similarity and word mover's distance (WMD) methods. The results indicate that the Siamese Manhattan hybrid LSTM-CNN model outperforms the other models, with a similarity score of 1 for each sentence pair, signifying identical semantic meaning.
EDUKASI PENGEMBANGAN TANAMAN BERPOTENSI SEBAGAI HERBAL BERNILAI EKONOMI BAGI KELOMPOK WANITA TANI DI DESA LIMAN BENAWI Nurhasanah, Nurhasanah; Asmiati, Asmiati; Junaidi, Junaidi; Lumbanraja, Favorisen R.; Nurcahyani, Nuning
BUGUH: JURNAL PENGABDIAN KEPADA MASYARAKAT Vol. 4 No. 1 (2024): Maret 2024
Publisher : Badan Pelaksana Kuliah Kerja Nyata Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/buguh.v4n1.2527

Abstract

Tanaman Herbal dikenal sebagai tanaman yang memiliki peran penting dalam kesehatan manusia. Jenis tanaman ini memiliki peluang cukup besar untuk dikembangkan menjadi usaha dan diharapkan dapat meningkatkan ekonomi masyarakat. Kegiatan Pengabdian ini bertujuan untuk memberikan edukasi kepada Kelompok Wanita Tani Desa Liman Benawi dalam pengembangan Tanaman yang berpotensi sebagai herbal bernilai ekonomi. Metode yang dilakukan meliputi ceramah, diskusi dan demonstrasi pengenalan tanaman berpotensi herbal. Hasil kegiatan menunjukkan adanya peningkatan pengetahuan rata-rata sebesar 53,3 % untuk setiap parameter Tujuan Instruksional Khusus (TIK) meliputi jenis tanaman pekarangan yang berpotensi herbal, manfaatnya serta jenis tanaman herbal berupa rempah dan sayuran. Kegiatan ini diharapkan dapat menginisiasi kelompok KWT lainnya dalam upaya pengenalan tanaman berpotensi herbal yang memiliki nilai ekonomis, serta dapat membudidayakannya dengan memanfaatkan lahan pekarangan yang ada.
1D and 2D Feature Extraction Based on AAC and DC Protein Descriptors for Classification of Acetylation in Lysine Proteins using Convolutional Neural Network Faisal, Mohammad Reza; Adawiyah, Laila; Saragih, Triando Hamonangan; kartini, Dwi; Herteno, Rudy; Lumbanraja, Favorisen Rosyking; Handayani, Lilies; Solechah, Siti Aisyah
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 4 (2024): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i4.458

Abstract

Post-Translational Modification (PTM) denotes a biochemical alteration observed in an amino acid, playing crucial roles in protein activity, functionality, and the regulation of protein structure. The recognition of associated PTMs serves as a fundamental basis for understanding biological processes, therapeutic interventions for diseases, and the development of pharmaceutical agents. Using computational approaches (in silico) offers an efficient and cost-effective means to identify PTM sites swiftly. The exploration of protein classification commences with extracting protein sequence features that are subsequently transformed into numerical features for utilization in classification algorithms. Feature extraction methodologies involve using protein descriptors like Amino Acid Composition (AAC) and Dipeptide Composition (DC). Yet, these approaches exhibit a limitation by neglecting crucial amino acid sequence details. Moreover, both descriptor techniques generate a limited number of 1-dimensional (1D) features, which may not be ideal for processing through the Convolutional Neural Network (CNN) classification method. This investigation presents a novel approach to enhance feature diversity through protein sequence segmentation techniques, employing adjacent and overlapping segment strategies. Furthermore, the study illustrates the organization of features into 1D and 2D formats to facilitate processing through 1D CNN and 2D CNN classification methodologies. The findings of this research endeavour highlight the potential for enhancing the accuracy of acetylation classification in lysine proteins through the multiplication of protein sequence segments in a 2D configuration. The highest accuracy achieved for AAC and DC-based feature extraction methods is 77.39% and 76.75%, respectively.
PELATIHAN PENGGUNAAN MICROSOFT WORD UNTUK GURU SEKOLAH DASAR DI SDN MARGOREJO, KEC. JATI AGUNG, KABUPATEN LAMPUNG SELATAN Lumbanraja, Favorisen R.; Sholehurrohman, Ridho; Parabi, M. Iqbal; Ilman, Igit Sabda; Muhaqiqin, Muhaqiqin
Laporan Upaya Nyata Inovasi Ilmu Komputer Vol. 2 No. 01 (2024)
Publisher : FMIPA Unila

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/lunik.v2i01.23

Abstract

Kegiatan pelatihan Microsoft Word ini dirancang untuk memberikan peserta pemahaman mendalam dan keterampilan praktis dalam menggunakan perangkat lunak pengolah kata Microsoft Word secara efisien dan efektif. Pelatihan akan mencakup penguasaan fitur dasar hingga tingkat lanjutan, termasuk penggunaan gaya dokumen, tata letak halaman, objek visual, mail merge, dan kolaborasi dalam pengeditan dokumen bersama. Tujuan utama pelatihan ini adalah meningkatkan produktivitas dan kualitas kerja peserta dengan memanfaatkan potensi penuh Microsoft Word. Peserta akan diajarkan teknik-teknik efisien, pintasan keyboard, dan fitur-fitur otomatisasi untuk menghemat waktu dalam pembuatan dan penyuntingan dokumen. Mereka akan belajar tentang penggunaan gaya dokumen untuk memastikan konsistensi tampilan dan format, serta bagaimana mengatur objek visual seperti gambar, tabel, dan grafik. Selain itu, pelatihan akan membantu peserta mengatasi tantangan dalam kolaborasi tim dengan pengenalan alat kolaborasi dalam Microsoft Word. Peserta juga akan memahami teknik penggabungan data melalui mail merge dan penerapan penggunaan template untuk berbagai jenis dokumen.Pelatihan ini akan memberikan manfaat nyata dalam dunia kerja dan pendidikan. Peserta akan mampu menghasilkan dokumen berkualitas tinggi yang memenuhi standar profesional, mengurangi waktu yang dihabiskan untuk tugas pengolahan kata, dan meningkatkan efisiensi dalam proses kerja. Selain itu, peserta akan memiliki kemampuan yang relevan dengan tuntutan pekerjaan dan pendidikan saat ini, memberi mereka keunggulan kompetitif dalam berbagai situasi. Melalui kegiatan pelatihan ini, peserta akan memperoleh kepercayaan diri yang lebih tinggi dalam mengoperasikan Microsoft Word dan menghadapi tugas-tugas yang melibatkan pengolahan kata. Pelatihan ini bukan hanya investasi dalam keterampilan teknis, tetapi juga investasi dalam pengembangan diri yang akan membawa manfaat jangka panjang dalam karir dan kehidupan sehari-hari. Kegiatan PKM ini bertujuan untuk melakukan transfer pengetahuan dalam mengimplementasikan aplikasi MS.Word sebagai wujud menjadi masyarakat unggul dan guru penggerak.
A New Feature Extraction Approach in Classification for Improving the Accuracy of Proteins Damayanti, -; Lumbanraja, Favorisen Rosyking; Junaidi, Akmal; Sutyarso, -; Susanto, Gregorius Nugroho; Megawaty, Dyah Ayu
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.2589

Abstract

Proteins play a vital role in life as essential macromolecules, consisting of linear heteromeric biopolymers formed by amino acids covalently bonded through peptide bonds. They contribute to cell development and bolster the body's defense mechanisms. Post-translational modification processes, such as glycosylation, are necessary for proteins to function optimally. Glycosylation involves adding sugar groups to proteins, playing a critical role in various protein folding processes. Dysregulation of protein glycosylation can lead to diseases like Alzheimer's and cancer. Manual classification of glycosylated proteins is time-consuming, necessitating a faster approach. This study aims to expedite glycosylated protein classification using novel methods like AAindex, CTD, SABLE, hydrophobicity, and PseAAC for increased accuracy, comparing them with existing approaches. The dataset comprises protein sequences sourced from the openly accessible UniProt database. Results demonstrate that glycosylated protein prediction achieved 100% accuracy, surpassing previous approaches. Several features contributed to this improvement, with Hydrophobicity making a significant contribution at 24%, and PseAAC making the most significant contribution at 40% among the five extraction methods developed.
Model Long Short-Term Memory dalam Penerjemahan Mesin Bahasa Lampung Dialek Api ke Bahasa Indonesia Akbar, Mohammed Raihan; Muludi, Kurnia; Junaidi, Akmal; Lumbanraja, Favorisen Rosyking
Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol 16, No 1 (2025): JURNAL SIMETRIS VOLUME 16 NO 1 TAHUN 2025
Publisher : Fakultas Teknik Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24176/simet.v15i2.13394

Abstract

Penelitian ini mengeksplorasi penerjemahan bahasa Lampung dialek Api ke bahasa Indonesia menggunakan Long Short-Term Memory (LSTM). Bahasa Lampung, salah satu bahasa daerah di Indonesia, terancam punah sehingga penting untuk dilestarikan dan dimanfaatkan. Penelitian ini bertujuan mengembangkan model penerjemahan otomatis untuk memfasilitasi pembelajaran dan penggunaan bahasa Lampung. Dataset terdiri dari kalimat paralel bahasa Lampung dan bahasa Indonesia yang dibagi menjadi data pelatihan dan pengujian. Model LSTM memiliki lapisan embedding, LSTM, dan dense, dilatih dengan optimizer Adam dan fungsi loss categorical crossentropy. Evaluasi model menggunakan metrik BLEU menunjukkan kualitas terjemahan yang baik dengan skor BLEU tertinggi sekitar 0.8 untuk 1-grams, 0.75 untuk 1-2-grams, 0.65 untuk 1-3-grams, dan 0.55 untuk 1-4-grams pada set latih. Pada set uji, skor BLEU tercatat 0.5 untuk 1-grams, 0.35 untuk 1-2-grams, 0.25 untuk 1-3-grams, dan 0.2 untuk 1-4-grams. Hasil ini menunjukkan bahwa model mampu menangkap konteks bahasa dengan baik pada data pelatihan dan masih mempertahankan performa yang memadai pada data uji. Selama pelatihan, loss model tercatat mengalami penurunan yang konsisten, menandakan peningkatan kemampuan model dalam memprediksi terjemahan yang benar. Hasil penelitian menunjukkan bahwa LSTM dapat diterapkan untuk penerjemahan bahasa Lampung-Indonesia, dan pendekatan ini memiliki potensi besar untuk diterapkan pada bahasa daerah lainnya yang juga terancam punah. Dapat dilakukan perbaikan dan peningkatan pada penelitian ini, seperti penggunaan arsitektur model yang lebih kompleks, pemanfaatan dataset yang lebih besar dan bervariasi, serta penerapan teknik augmentasi data untuk meningkatkan kinerja model. Hasil penelitian ini diharapkan menjadi referensi untuk penelitian lebih lanjut di bidang penerjemahan mesin dan pelestarian bahasa.
The Expert System for Diagnosing Pest and Disease in Pineapple Plant Using the Iterative Deepening Search (IDS) Method on the Android Platform Amalia, Ayu; Junaidi, Akmal; Sudarsono, Hamim; Lumbanraja, Favorisen R.
Integra: Journal of Integrated Mathematics and Computer Science Vol. 1 No. 1 (2024): March
Publisher : Magister Program of Mathematics, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/integrajimcs.2024119

Abstract

This research was conducted to design and develop pineapple pests and diseases diagnosis expert system with Iterative Deepening Search (IDS). This expert system runs on android platform. The certainty factor of this expert system is initialized by an expert and the final certainty factor is computed by the system. The data used in this expert system consist of 5 types of pineapple pests, 6 types of pineapple diseases. 31 types of symptoms and 11 types of rules are used to diagnose pineapple pests and diseases. To validate this expert system, two types of tests were conducted, which are expert system verification and system evaluation by users. Expert system verification was conducted by comparing 10 results from the diagnosis system and the results of the diagnosis by an expert. The compare result shows that the expert system result 100% is similar to the result of the expert. To evaluate the system, 30 respondents were asked to evaluate using questionnaires, which were grouped into three groups, i.e. group I (pineapple experts), group II (pineapple farmers and agriculture students) and group III (computer science students). All three stated this expert system runs well (75.56%, 72.44%, and 79.83% respectively).
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 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 Hadi, Normi Abdul Hamim Sudarsono . Hdiana, Yazid Zinedine Heningtyas, Yunda Ilman, Igit Sabda Indah Pasaribu Ira Hariati Br Sitepu Irawati, Anie Rose 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 Nova Ayu Lestari Siahaan Nuning Nurcahyani 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, - Syangap Diningrat Sitompul TANJUNG, AKBAR RISMAWAN Tiyara Saghira Tristiyanto Tristiyanto Wamiliana Warsono Warsono Warsono Warsono Warsono YOHANA TRI UTAMI, YOHANA TRI Zuliana Nurfadlilah