Article Per Year (5 Year)
p-Index From 2021 - 2026
4.71
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) IJCCS (Indonesian Journal of Computing and Cybernetics Systems) TEKNIK INFORMATIKA JURNAL SISTEM INFORMASI BISNIS Bulletin of Electrical Engineering and Informatics POSITIF Journal of Tropical Biodiversity and Biotechnology KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) JOIV : International Journal on Informatics Visualization Jurnal Komputasi Jurnal SOLMA Jurnal Teknoinfo Jurnal Elektronika dan Telekomunikasi Jurnal Tekno Kompak Indonesian Journal of Electrical Engineering and Computer Science Jurnal Pengabdian Kepada Masyarakat (JPKM) Tabikpun Journal of Applied Data Sciences Journal of Multidisciplinary Applied Natural Science Buguh: Jurnal Pengabdian Kepada Masyarakat Jurnal Pepadun AMMA : Jurnal Pengabdian Masyarakat Integra: Journal of Integrated Mathematics and Computer Science Journal of Digital Market and Digital Currency
Admi Syarif
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 Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Nursing Physics Social Sciences Other

Published : 36 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 36 Documents
Search

 1   2   3   4 
Application of Random Forest Method Classification for Glycosylation in Lysine Protein Sequences Fitriyana, Silfia; Syarif, Admi; Rossyking, Favorisen; Faisal, Mohammad Reza
Integra: Journal of Integrated Mathematics and Computer Science Vol. 1 No. 2 (2024): July
Publisher : Magister Program of Mathematics, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/integrajimcs.20241218

Abstract

Grouping glycosylated lysine proteins into groups according to the type of glycosylation seen in the lysine protein sequence is known as glycosylation in the lysine protein sequence. In this work, the sensitivity, specificity, accuracy, and Matthew’s correlation coefficient (MCC) of the random forest approach for classifying the glycosylation of lysine protein sequences were examined. With 214 positive and 406 negative data, the lysine protein dataset derived from benchmark data contains 620 total proteins with a protein length of 15 sequences. 90% of the dataset is used for training, while 10% is used for testing. Using the R package BioSeqClass version 1.44.0, feature extraction employed protein descriptors, specifically AA Index, CTD, and PseAAC, with a total of 60 features. The Random Forest classification algorithm was used to reprocess the results with Mtry values of 4, 8, and 16. The number of trees (ntree) was randomly set to 250, 500, 750, and 1000. The best results were achieved with a dataset split of 90% training data and 10% test data, using Mtry of 42 and 1000 trees, resulting in 89.97% sensitivity, 92.79% specificity, 80.76% MCC, and 90.42% accuracy. These results demonstrate that the combination of feature extraction and the Random Forest algorithm is effective in classifying lysine proteins.
Sentiment Analysis of Twitter Discussions About Lampung Robusta Coffee: A Comparative Study of Machine Learning Algorithms with SVM as The Optimal Model Yuniarthe, Yodhi; Syarif, Admi; Shofi, Imam Marzuki; Fatimah Fahurian
JURNAL TEKNIK INFORMATIKA Vol. 18 No. 2: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v18i2.41316

Abstract

Lampung Robusta coffee is an important commodity in Indonesia, particularly in terms of local economic potential and global recognition. However, public perception of this product on social media, particularly Twitter, remains underexplored. This study addresses the need for a deeper understanding of consumer sentiment towards Lampung Robusta coffee, which could inform branding and marketing strategies. To approach this issue, we used five supervised machine learning algorithms-KNN, Naive Bayes, SVM, Decision Tree, and Logistic Regression-to perform sentiment classification on a dataset of tweets containing relevant keywords. The dataset was pre-processed using standard natural language processing techniques, including tokenization, stopword removal, and TF-IDF feature extraction. The SVM achieved the best performance on the unbalanced dataset for all metrics, with high and consistent accuracy and F1 scores. Logistic regression followed closely with similarly strong and stable results. Therefore, SVM is recommended as the final model. These results suggest that machine learning approaches can effectively classify sentiment in social media discussions about regional agricultural products and that random forest may provide the most robust performance in this context  
Market Analysis of NFT Integration in Video Games Hasibuan, Muhammad Said; Putra, Arie Setya; Syarif, Admi; Mahfut; Sulistiyanti, Sri Ratna
Journal of Digital Market and Digital Currency Vol. 2 No. 2 (2025): Regular Issue June 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v2i2.33

Abstract

The integration of Non-Fungible Tokens (NFTs) into the gaming industry has introduced a novel economic model, reshaping monetization strategies and player engagement. This paper analyzes the market potential of NFT integration in video games through a comprehensive approach combining market segmentation, trend analysis, and predictive modeling. Using historical sales data from various genres and platforms, the research identified key segments that show high potential for NFT adoption, particularly action, role-playing, and sports games on mainstream platforms such as PlayStation and Xbox. The market segmentation, achieved through K-Means clustering, revealed distinct groups of video games based on genre, platform, and regional sales performance. Trend analysis using time series models like ARIMA and Prophet highlighted emerging and declining popularity across different genres and platforms. The study also applied predictive modeling techniques, including Random Forest and Gradient Boosting, to forecast the potential success of NFTs in specific game genres. The models demonstrated strong performance, with low mean absolute error (MAE) and root mean squared error (RMSE), confirming that high-engagement genres are likely to benefit most from NFT integration. The findings suggest that NFTs can enhance player experiences by offering unique, tradable in-game assets, thus creating new revenue streams for developers. The paper concludes by recommending strategies for NFT implementation, targeting high-potential genres and platforms, and addressing regional market preferences. Limitations related to data constraints and emerging trends are discussed, and future research directions are proposed, focusing on consumer sentiment analysis and real-world case studies of NFT integration in video games.
Implementasi Pengendalian Penyakit Anggrek Alam di Kebun Raya Liwa Melalui Uji Antagonistik dan Induksi Ketahanan Mikoriza Endofit Mahfut, Mahfut; Syarif, Admi; Wahyuningsih, Sri; Salsabila, Diana; Asmanto, Budi; Rahardi, Agus
Jurnal SOLMA Vol. 14 No. 3 (2025)
Publisher : Universitas Muhammadiyah Prof. DR. Hamka (UHAMKA Press)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22236/solma.v14i3.20480

Abstract

Pendahuluan: Anggrek alam merupakan salah satu koleksi flora asli di Kebun Raya Liwa yang merupakan flora endemik di Sumatera bagian selatan. Berdasarkan analisis situasi, diketahui beberapa individu anggrek alam menunjukkan gejala terinfeksi penyakit. Pengendalian penyakit sampai saat ini masih menggunakan pestisida yang membahayakan lingkungan. Hasil penelitian kami sebelumnya telah menghasilkan metode dan solusi baru dalam pengendalian infeksi penyakit pada anggrek alam melalui uji antagonistik induksi ketahanan mikoriza endofit. Metode tersebut dinilai terbukti sangat efektif dalam perlindungan anggrek alam melalui penurunan infeksi dan penyebaran penyakit.  Kegiatan Pengabdian Kepada Masyarakat-Diseminasi Hasil Riset (PkM-DHR) ini bertujuan untuk mengimplimentasikan hasil penelitian kami sebelumnya yaitu implementasi pengendalian infeksi penyakit pada anggrek alam melalui uji antagonistik induksi ketahanan mikoriza endofit yang lebih ramah lingkungan, murah, efektif, dan efisien. Metode: Metode rumusan pemecahan masalah dilakukan melalui sosialisasi, pelatihan, pendampingan, dan evaluasi. Hasil:  Hasil kegiatan ini menunjukkan menunjukkan rata-rata peningkatan kemampuan peserta sebesar 23%, dengan peningkatan tertinggi pada materi Pemberdayaan Masyarakat dalam Konservasi Anggrek (26%), diikuti Keanekaragaman dan Penyakit Anggrek (20%), Pengendalian Hayati dengan Uji Antagonistik (19%), dan Peran Mikoriza Endofit dalam Ketahanan Anggrek (18%). Kegiatan ini menjadi langkah awal dalam upaya konservasi anggrek alam dan pemberdayaan masyarakat sekitar, yang ke depannya memerlukan pendampingan berkelanjutan baik secara luring maupun daring untuk memastikan penerapan metode pengendalian ini secara optimal dan mandiri. Kesimpulan: Kegiatan ini terbukti pengendalian penyakit ramah lingkungan, sehingga direkomendasikan adanya pelatihan lanjutan untuk memastikan penerapan metode ini dapat dilakukan secara optimal, mandiri, dan berkelanjutan sebagai upaya konservasi anggrek alam di Kebun Raya Liwa.
Feature Selection and Class Imbalance Machine Learning for Early Detection of Thyroid Cancer Recurrence: A Performance-Based Analysis Wantoro, Agus; Caesarendra, Wahyu; Syarif, Admi; Soetanto, Hari
Jurnal Elektronika dan Telekomunikasi Vol 25, No 2 (2025)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jet.758

Abstract

Early detection of thyroid cancer recurrence is a crucial factor in patient survival and treatment effectiveness. Misdetection results in disease severity, high cost, recovery time, and decreased service quality. In addition, the main challenges in developing a Machine Learning (ML)-based detection decision support system are class imbalance in medical data and high feature dimensions that can affect model accuracy and efficiency. This study proposes a feature selection-based approach and class imbalance handling to improve the performance of early detection of Thyroid cancer. Several feature selection techniques, such as Information Gain (IG), Gain Ratio (GR), Gini Decrease (GD), and Chi-Square (CS), can select features based on weighted ranking. In addition, to overcome the imbalanced class distribution, we use the Synthetic Minority Over-Sampling Technique (SMOTE). ML classification models such as k-NN, Tree, SVM, Naive Bayes, AdaBoost, Neural Network (NN), and Logistic Regression (LR) are tested and evaluated based on a confusion matrix, including accuracy, precision, recall, time, and log loss. Experimental results show that the combination of imbalanced class handling strategies significantly improves the prediction performance of ML algorithms. In addition, we found that the combination of CS+NN feature selection techniques consistently showed optimal performance. This study emphasizes the importance of data pre-processing and proper algorithm selection in the development of a machine learning-based thyroid cancer detection system.
PELATIHAN PEMBUATAN BIOSAKA SEBAGAI ALTERNATIF PUPUK PERTANIAN DI KAMPUNG SRIWIJAYA KECAMATAN UMPU SEMENGUK KABUPATEN WAY KANAN Yulia Kusuma Wardani; Syarif, Admi; Agustina Rahayu; Azi Mediantara; Faiqa Marina; Greacella Risky Amanda; Michelle Jovelyna; Muhammad Tegar Sabilillah; Wildhan Wahyudi
BUGUH: JURNAL PENGABDIAN KEPADA MASYARAKAT Vol. 5 No. 4 (2025): Desember 2025
Publisher : Badan Pelaksana Kuliah Kerja Nyata Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/buguh.v5n4.2499

Abstract

Biosaka terdiri dari dua kata, yaitu "Bio" dan "Saka". "Bio" memiliki arti terkait hayati atau tumbuhan,sementara "Saka" merujuk pada usaha menyelamatkan alam kembali ke alam. Biosaka adalah elisitor atausenyawa biologis yang merangsang peningkatan produksi fitoaleksin ketika diterapkan pada tanaman. Artikel iniberupaya memberikan gambaran dan penjelasan terkait program kerja pembuatan biosaka, meliputi proses,tahapan, hasil, dan manfaatnya. Penulisan artikel ini menggunakan pendekatan kualitatif dengan dengan modeldeskriptif-eksplanatif. Hasilnya, masyarakat menerima pemahaman dan wawasan baru dalam hal pemanfaatantumbuhan rerumputan yang ada disekitar tempat tinggal. Selain itu, masyarakat dapat mengurangi penggunaanpupuk kimia dan pupuk buatan pabrik yang umumnya sudah digunakan oleh masyarakat kampung, sehinggadapat meminimalkan pengeluaran petani dalam proses bercocok tanam. Artikel ini berkontribusi dalamkeilmuan pertanian berkelanjutan serta praktek dan strategi pengimplementasian poin SDGs.
Co-Authors Agus Rahardi, Agus Agus Wantoro Agus Wantoro AGUSTINA RAHAYU Akmal Junaidi Ami Zuraida Andrian, Rico Anggi Puspitasari Anggun N. Azizah Ani Kurniawati Aqshal Dwi Setiawan Arafia Isnayu Akaf Ari Ardianto Arie Setya Putra Aristoteles, Aristoteles Asmanto, Budi Ayu Nadila Ayu Sangging, Putu Ristyaning Azi Mediantara Bambang Hermanto Dedy Miswar Deswita Sari Dimas A. Dhafa Dwi Sakethi Erika Fadia Salsabila Faiqa Marina Fatimah Fahurian Fazri, Yudistira Febi Eka Febriansyah Fitriyana, Silfia Ghraito Arip Greacella Risky Amanda Hari Soetanto Heni Sulistiani Heningtyas, Yunda Iva Mutiara Indah Kendari, Putri Khairun Nisa Krisna Rendi Awalludin Kurnia Muludi Kurnia Muludi Lumbanraja, Favorisen R M Said Hasibuan M. Juandhika Rizky Machudor Yusman Mahfut Maya Asterita Michelle Jovelyna Mohammad Surya Akbar Muhammad Irfan Ardiansyah Muhammad Jamaludin Muhammad Reza Faisal, Muhammad Reza Muhammad Rizki Muhammad Tegar Sabilillah Nabila Z. Muhammad Ni K. Aprilliani Nisa Berawi, Khairun Noverina Rahmaniyanti Novita Dwilestari Nur Indriani Prabowo, Rizky Prabowo, Rizky Putri Ayu Penita Qory Aprilarita Raden Mohamad Herdian Bhakti Rahmat Safe'i Raras Silviana Redy Susanto, Erliyan Salsabila, Diana Shofi, Imam Marzuki Shofiana, Dewi Asiah Sintiya Paramitha Sri Ratna Sulistiyanti Sri Wahyuningsih Sugaluh Yulianti Sukamto, Ika Sumiyarsi Sumaryo Gitosaputro Susiyanti, Endah Sutyarso Sutyarso Syachrul Priyo Wibowo TANJUNG, AKBAR RISMAWAN Timotius Pascha Tristiyanto Tundjung Tripeni Handayani Wahyu Caesarendra Warsito Warsito Wildhan Wahyudi Wulansari, Ossy Endah Dwi Yarmaidi Yarmaidi Yoannisa Egeustin Yodhi Yuniarthe Yokie Rahman Yulia K. Wardani Yulia Kusuma Wardani Yuliyanto, Kurniawan Dwi Zahra, Rizka Aulia