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All Journal Al-Jabar : Jurnal Pendidikan Matematika MANAJERIAL Desimal: Jurnal Matematika BERDAYA: Jurnal Pendidikan dan Pengabdian Kepada Masyarakat Islamic Journal of Integrated Science Education MDP Student Conference Jurnal Pengabdian Masyarakat Terapan
Farid, Fajri
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Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Physics Public Health Social Sciences Other

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Strategi Manajemen Usaha “Batik Lochatara” Berbasis Budaya Lokal dan Produk Unggulan di Kediri Ridwanulloh, M. Ubaidillah; Pangesti, Aliffanur Budi; Sya’diyah, Khalimatus; Farid, Fajri; Siatan, Mairizal Salehudin
Jurnal MANAJERIAL Vol 22, No 2 (2023): MANAJERIAL Volume 22 No. 2
Publisher : Universitas Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/manajerial.v22i2.60706

Abstract

This research aims to describe cultural and superior product-based Lochatara Batik business management strategies in Kediri City. In business management, there are steps taken by Lochatara Batik entrepreneurs to increase the competitiveness and quality of their products. The research method used is a qualitative method with a case study approach, which aims to obtain in-depth data regarding views or perceptions and implementation of Lochatara Batik business management strategies. Data sources were obtained through observation, interviews, and documentation. The results of the business management carried out include planning or preparation, organizing a reliable team or human resources, producing quality batik cloth with various patterned images, and evaluation involving the community to be able to produce the batik that the community wants. Marketing methods are carried out by utilizing E-Commerce Platforms and digital Social Media Platforms, building collaborations and partnerships, strengthening branding and promotions, and actively participating in various exhibition events. The results of the business management strategy show consistent growth from year to year. As a batik that focuses on Kediri cultural patterns, in 2018 Lochatara batik also received the Radar Kediri Award as Cultural Batik and Shopping Festival Award in 2020. By prioritizing product quality and innovative design, Lochatara Batik has succeeded in attracting interest and satisfying customers from around the world. the general public and tourists
Improvement of Creative Thinking Ability through Problem-Based Learning with Local Culture Based on Students’ Gender and Prior Mathematics Ability Ramadhani, Rahmi; Farid, Fajri; Lestari, Fitria; Machmud, Amir
Al-Jabar: Jurnal Pendidikan Matematika Vol 11 No 1 (2020): Al-Jabar: Jurnal Pendidikan Matematika
Publisher : Universitas Islam Raden Intan Lampung, INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/ajpm.v11i1.4961

Abstract

The purpose of this study was to determine the increase of students' creative thinking, which taught using problem-based learning with local culture (PBL-Local Culture). In this study also saw the interaction between students' gender and students' prior mathematics ability. This type of research is quasi-experiment research, using pre-test post-test control group design. Data were analyzed using SPSS 25 through Two-Way ANOVA. The result shows that increasing the creative thinking abilities of students taught using problem-based learning with local culture is significantly higher than the creative thinking abilities of students taught using classical learning. Based on the result, we found that the use of problem-based learning local culture (PBL-Local Culture) offered the opportunity to give student new experience to solving real problems in their daily life, primarily related in their local culture. Students can describe how to solve the daily life problem with mathematically modelling. This learning model has given the facility to students at the end to improve their creative thinking ability. Students make the new model of problem-solving and finally they can solve that problem with their model. This study also found that the factor of students' gender and students' prior mathematics ability has not given the effect of students' creative thinking ability. It means that there is no gap in gender and the contribution of students' prior mathematics ability in students' academic skills. Based on this study, we recommendation of this model to using in other subjects in the learning class and to improving other learning ability, not limited in creative thinking ability.    
Diabetes risk prediction using logistic regression model Rassiyanti, Linda; Farid, Fajri; Pitri, Rizka
Desimal: Jurnal Matematika Vol. 8 No. 1 (2025): Desimal: Jurnal Matematika
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/djm.v8i1.26493

Abstract

This study aims to analyze the factors that contribute to diabetes using the logistic regression method. The data used in this study include variables of number of pregnancies, glucose levels, blood pressure, skin thickness, insulin levels, body mass index (BMI), family history of diabetes, and age. The logistic regression model was applied to determine the effect of each variable on the likelihood of a person having diabetes. Evaluation of model performance was carried out using the ROC (Receiver Operating Characteristic) curve, and the results obtained showed an AUC value of 0.8391, which indicated a very good classification ability of the model. The results of the analysis showed that the number of pregnancies, glucose levels, blood pressure, BMI, and family history of diabetes had a significant effect on the risk of diabetes.
Multi-objective bees algorithm for portfolio diversification Farid, Fajri; Linda Rassiyanti; Rohmi Dyah Astuti; Ade Lailani
Desimal: Jurnal Matematika Vol. 8 No. 2 (2025): Desimal: Jurnal Matematika
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/mttkw723

Abstract

Portfolio diversification is the practice of spreading investments across different types of stocks or sectors to reduce overall risk. The basic principle is that the poor performance of one stock asset can be offset by the satisfactory performance of another stock asset. This study uses the Bees Algorithm for portfolio optimization problems, aiming to discover the combination of stock proportions in a portfolio that maximizes stock returns and minimizes risk. Then, the Sharpe ratio value is calculated and compared with conventional methods. The expected return, risk, and Sharpe ratio values for the portfolio generated using the Bees algorithm are 0.178007%, 2.353956%, and 0.0663484322, respectively. According to the results, the Bees Algorithm had better results and performance than conventional methods. As a result, the Bees Algorithm outperforms conventional approaches.
Pengenalan dan Implementasi Sistem Smart Lighting Berbasis IoT melalui Aplikasi App Inventor sebagai Media Edukasi Teknologi bagi Siswa SMA Vidia, Vidia; Ronal, Ronal; Farid, Fajri; Dyah Astuti, Rohmi; Tamaro Nadeak, Christyan; Yustisia Sari, Rizki; Fauzi Dzaki Arif, Sofyan; Satria, Eggi; Andriana Putra, Randa; Hilal Kurniawan, Danang
Jurnal Pengabdian Masyarakat Terapan Vol 2 No 3 (2025): JUPITER Desember 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jupiter.2.3.83

Abstract

Internet of Things (IoT), sebagai konsep penghubung perangkat fisik melalui internet secara otomatis, membuka peluang signifikan untuk peningkatan efisiensi dan produktivitas di berbagai sektor. Meskipun relevansinya tinggi, literasi digital di kalangan pelajar masih terbatas. Untuk mengetasi kesenjangan ini dan meningkatkan pemberdayaan siswa, kegiatan pengabdian masyarakat ini dilaksanakan selama satu hari di SMA Al Huda Jati Agung, bekerja sama dengan Kepala Sekolah dan guru. Kegiatan ini berfokus pada pengenalan dan demonstrasi implementasi sederhana teknologi IoT pada konsep Smart Lighting menggunakan App Inventor. Metode yang digunakan adalah presentasi interaktif diikuti demonstrasi langsung perangkat. Dampak kegiatan dievaluasi melalui Pre-test dan Post-test, yang secara statistik (Uji Paired Sample T-Test, ) menunjukkan peningkatan signifikan pemahaman siswa, dengan rata-rata kenaikan skor sebesar 14%. Keberhasilan ini tidak hanya menumbuhkan minat, tetapi secara efektif meletakkan dasar bagi pengembangan keterampilan digital siswa, berkontribusi pada kesiapan mereka menghadapi tantangan era digital, dan mendukung keberlanjutan pemanfaatan teknologi untuk solusi kehidupan sehari-hari.
Enhancing multiclass SVM classification using a hybrid directed acyclic graph and rest-vs-rest strategy Nadeak, Christyan Tamaro; Farid, Fajri; Rassiyanti, Linda; Siahaan, Arielva Simon; Putri, Lutfia Aisyah
Desimal: Jurnal Matematika Vol. 8 No. 3 (2025): Desimal: Jurnal Matematika
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/djm.v8i3.202529469

Abstract

This study proposes a modified Directed Acyclic Graph Support Vector Machine (DAG-SVM) using a Rest-vs-Rest (RvR) strategy to address the multiclass classification problem in the Hepatitis C dataset from Kaggle, which contains four diagnostic categories with a highly imbalanced class distribution, with class sample sizes of 540, 24, 21, and 30, respectively. The aim of this study is to examine how hierarchical decision structures interact with extreme class imbalance in SVM-based multiclass classification. The method is implemented through three fixed hierarchical decision schemes {0,1} vs. {2,3}, {0,2} vs. {1,3}, and {0,3} vs. {1,2} which restructure the decision flow of conventional DAG-SVM. Experimental evaluation shows that although the proposed schemes achieve relatively high overall accuracy (0.91–0.93), the precision, recall, and F1-scores for minority classes remain extremely low. These findings offer a new empirical insight into how class imbalance propagates through the DAG hierarchy, leading to early elimination of minority classes, and highlight the need for imbalance-handling techniques such as resampling, cost-sensitive learning, or synthetic data generation. The contribution of this work lies in demonstrating the limitations of DAG-RvR under severe imbalance and providing a structured evaluation that can guide future improvements for more reliable multiclass recognition.
Workshop Pembuatan Sistem Monitoring Jaringan Sederhana Menggunakan Python bagi Siswa SMKN 4 Bandar Lampung Wisnubroto, M. Syamsuddin; Yuliana, Yuliana; Rassiyanti, Linda; Lailani, Ade; Farid, Fajri; Nadeak, Christyan Tamaro; Nurjanah, Fitri; Suciati, Indah; Kurnia, Rian; Lestari, Yusni Puspha; Setiawan, Dewi Indra
BERDAYA: Jurnal Pendidikan dan Pengabdian Kepada Masyarakat Vol 8 No 2 (2026)
Publisher : LPMP Imperium

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36407/berdaya.v8i2.1810

Abstract

This community service program was conducted to strengthen vocational students' competencies in network monitoring using Python. The partner school's main challenge was that networking lessons were still centered on hardware-oriented tools such as Mikrotik and Cisco, while software-based monitoring skills had not been introduced systematically. The program took the form of a workshop at SMKN 4 Bandar Lampung on 24 September 2025 and combined short lectures, demonstrations, guided practice, and mini projects. The training module covered basic networking concepts, connectivity and server status, bandwidth and latency, Python fundamentals, and the use of requests, psutil, socket, subprocess, and pandas to build a simple network monitoring system. Evaluation was conducted descriptively using pre-tests, post-tests, and practical assessment. The results showed that the mean pre-test accuracy of 46% from 32 participants increased to 64% in the post-test completed, representing an 18 percentage-point gain. All students also completed the assigned Python-based monitoring practice successfully. The outputs included a training module, poster, short video, and press release to support sustainability and dissemination.
Klasifikasi Multikelas Varietas Kacang Kering Menggunakan Metode Hybrid SVM Berbasis DAG Nababan, Dinda; Nadeak, Christyan Tamaro; Rassiyanti, Linda; Farid, Fajri
MDP Student Conference Vol 5 No 2 (2026): The 5th MDP Student Conference 2026
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/mdp-sc.v5i2.14094

Abstract

This study analyzes the performance of three conventional SVM strategies, namely One-vs-One (OvO), One-vs-Rest (OvR), and Directed Acyclic Graph OvO (DAG-OvO), compared with the hybrid approach Directed Acyclic Graph Rest-vs-Rest (DAG-RvR) in the context of multiclass classification using the Dry Bean Dataset. All models are evaluated based on accuracy and macro metrics to measure the consistency of predictions between classes. The results show that both conventional and hybrid methods achieve the same high level of accuracy, namely 0.92, with Precision, Recall, and F1-score Macro values ​​that were also identical between approaches. The main difference between the approaches lies in computational efficiency. OvO and DAG-OvO show the fastest training time, while DAG-RvR is the most efficient method in the inference stage. These findings confirm that the hybrid DAG-RvR structure can accelerate the prediction process without compromising accuracy, making it worthy of consideration for applications that require fast inference.
Klasifikasi Multikelas Support Vector Machine dengan Hibrida Directed Acyclic Graph One Vs One dan Rest Vs Rest pada Klasifikasi Tingkat Obesitas Naufal, Daffa Ahmad; Nadeak, Christyan Tamaro; Rassiyanti, Linda; Farid, Fajri
MDP Student Conference Vol 5 No 2 (2026): The 5th MDP Student Conference 2026
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/mdp-sc.v5i2.14097

Abstract

This research is focused on analyzing how well different multiclass Support Vector Machine (SVM) classification methods can predict obesity levels. It also presents a new hybrid Directed Acyclic Graph Rest-vs-Rest (DAG-RvR) method as a better option. The study utilizes a dataset called the Obesity Risk Prediction Cleaned, which has information on seven different obesity categories. The methods being assessed include One-vs-One (OvO), One-vs-Rest (OvR), DAG-One-vs-One (DAG-OvO), and the new DAG-RvR method. For fine-tuning the parameters, GridSearchCV and the RBF kernel were used. The findings reveal that DAG-RvR achieves an accuracy of 0.91, which is similar to OvO and DAG-OvO, but it trains much quicker, taking just 0.3422 seconds. Even though its precision, recall, and F1-score are a bit lower than the pairwise methods, DAG-RvR still maintains reliable multiclass performance. In summary, this method strikes a good balance between achieving high accuracy and being efficient in computations.
Klasifikasi Varietas Beras Menggunakan Hybrid SVM Berbasis DAG–OVO dan RVR Leander, Marleta Cornelia; Nadeak, Christyan Tamaro; Rassiyanti, Linda; Farid, Fajri
MDP Student Conference Vol 5 No 2 (2026): The 5th MDP Student Conference 2026
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/mdp-sc.v5i2.14108

Abstract

This research proposes a hybrid Support Vector Machine (SVM) strategy for multiclass rice variety classification by combining Directed Acyclic Graph Rest-vs-Rest (DAG-RvR) with K-Means clustering. Five rice varieties were analyzed using 16 morphological and texture features extracted from the Rice Image Dataset. Three conventional SVM methods—One-vs-One (OvO), One-vs-Rest (OvR), and DAG-OvO—were evaluated as baselines. Two hybrid schemes were then developed: DAG-RvR K-Means–OvO and DAG-RvR K-Means–K-Means. Experimental results show that all methods achieve high accuracy of approximately 99%, indicating strong feature separability among rice varieties. However, the proposed DAG-RvR K-Means–OvO provides the most efficient performance, achieving the fastest training time while maintaining competitive testing speed and the highest accuracy of 0.99040. The findings demonstrate that integrating K-Means–based class partitioning with pairwise SVM classification improves computational efficiency without reducing predictive performance, making the hybrid approach suitable for fast and accurate multiclass classification tasks.
Co-Authors Ade Lailani Amir Machmud, Amir Andriana Putra, Randa Christyan Tamaro Nadeak Dyah Astuti, Rohmi Fauzi Dzaki Arif, Sofyan Fitria Lestari Fredi Ganda Putra Hilal Kurniawan, Danang Lailani, Ade Leander, Marleta Cornelia Lestari, Yusni Puspha Linda Rassiyanti M. Yogi Riyantama Isjoni Maulidi, Ikhsan Muhamad Syazali Nababan, Dinda Naufal, Daffa Ahmad Nurjanah, Fitri Pangesti, Aliffanur Budi Pitri, Rizka Putri, Lutfia Aisyah Rahmi Ramadhani Rassiyanti, Linda Rian Kurnia, Rian Ridwanulloh, M. Ubaidillah Rohmi Dyah Astuti Ronal, Ronal Satria, Eggi Setiawan, Dewi Indra Siahaan, Arielva Simon Siatan, Mairizal Salehudin Suciati, Indah Sya’diyah, Khalimatus Vidia, Vidia Vina Apriliani Wisnubroto, M. Syamsuddin Yuliana Yuliana Yustisia Sari, Rizki