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All Journal Desimal: Jurnal Matematika BERDAYA: Jurnal Pendidikan dan Pengabdian Kepada Masyarakat Smart Society: Community Service and Empowerment Journal Lattice Journal : Journal of Mathematics Education and Applied MDP Student Conference
Rassiyanti, Linda
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Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Mathematics Public Health Social Sciences Other

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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.
Empowering the Future: AI-Based Website Development Training to Boost High School Students' Creativity and Digital Skills Muthoharoh, Luluk; Yuliana, Yuliana; Rassiyanti, Linda; Lailani, Ade; Sofia, Ayu; Yulita, Tiara; Sasongko, Dharu Cahyoaji; Maharani, Khairunnisa; Rahman, Aditya
Smart Society Vol 5, No 1 (2025): June (2025)
Publisher : FOUNDAE (Foundation of Advanced Education)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/smartsociety.v5i1.658

Abstract

In the rapidly evolving digital era, Artificial Intelligence (AI) has become one of the key technologies playing a significant role in various fields, including website development. AI-based website development training is a strategic step to enhance the skills and creativity of the younger generation in the era of digitalization. This activity was conducted at SMA Al Huda, South Lampung, with the aim of equipping students with an understanding of basic concepts, steps for creating websites, and how AI can be utilized to enhance creativity in the digital world. The methods used included interactive presentations, demonstrations, hands-on practice, and discussions. The activity also assessed students' knowledge before and after the training through an interactive quiz using Quizizz. The results of the study showed a significant improvement in students' understanding of AI-based website development. In conclusion, this training program was effective in enhancing students' skills and creativity in the digitalization world, particularly in creating AI-based websites
Hubungan Sebab-Akibat Kecerdasan Interpersonal Terhadap Hasil Belajar Peserta Didik: SEM-PLS Pitri, Rizka; Irawan, Miranda Dimas; Rassiyanti, Linda
Lattice Journal : Journal of Mathematics Education and Applied Vol. 4 No. 2 (2024): Desember 2024
Publisher : Universitas Islam Negeri Sjech M. Djamil Djambek Bukittinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30983/lattice.v4i2.9069

Abstract

Interpersonal intelligence is a person's ability to work together and understand between individuals with others. The utilization of interpersonal intelligence in mathematics learning will have an impact on improving the quality of learning outcomes. However, currently the social interaction that occurs in educational units is still low. Thus, resulting in low quality of learning outcomes. Based on this, an analysis is needed to measure the causal relationship between interpersonal intelligence and learning outcomes using Structural Equation Modeling (SEM)-PLS. This study used 75 samples of grade X students at SMAN Martapura. The purpose of this research is to see the cause-and-effect relationship between interpersonal intelligence and learning outcomes. The method used was SEM-PLS method. The instruments used in the study consisted of essay questions and interpersonal intelligence questionnaire. This study shows that there is a significant causal relationship between interpersonal intelligence and students' learning outcomes in mathematics.   Kecerdasan interpersonal merupakan suatu kemampuan seseorang dalam bekerja sama dan memahami antar individual dengan yang lainnya. Pemanfaatan kecerdasan interpersonal dalam pembelajaran matematika akan berdampak pada peningkatan kualitas hasil belajar. Namun, saat ini interaksi sosial yang terjadi di satuan pendidikan masih rendah. Sehingga, mengakibatkan rendahnya kualitas hasil belajar. Berdasarkan hal tersebut, diperlukan suatu analisis untuk mengukur hubungan sebab-akibat antara kecerdasan interpersonal terhadap hasil belajar menggunakan Structural Equation Modeling (SEM)-PLS. Penelitian ini menggunakan 75 sampel siswa kelas X di SMAN Martapura. Tujuan penelitian ini adalah melihat hubungan sebab-akibat antara kecerdasan interpersonal terhadap hasil belajar. Metode yang digunakan adalah metode SEM-PLS. Instrumen yang digunakan dalam penelitian terdiri dari soal essay dan angket kecerdasan interpersonal. Penelitian ini menunjukkan bahwa terdapat hubungan sebab-akibat yang signifikan antara kecerdasan interpersonal terhadap hasil belajar peserta didik di bidang matematika.
Analisis Komparasi Model Peramalan Prophet Dan Arima Dalam Memprediksi Harga Saham Penutupan PT ANTM Anwar, Rohimatul; Rassiyanti, Linda
Lattice Journal : Journal of Mathematics Education and Applied Vol. 5 No. 1 (2025): Juni 2025
Publisher : Universitas Islam Negeri Sjech M. Djamil Djambek Bukittinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30983/lattice.v5i1.9478

Abstract

PT Aneka Tambang Tbk (ANTM) is a major mining company in Indonesia whose shares are actively traded on the Indonesia Stock Exchange. Its stock price is influenced by both internal and external factors. Time series forecasting methods, such as ARIMA and Prophet, are used to predict future stock movements. ARIMA is known for its flexibility and high prediction accuracy, while Prophet is capable of handling missing values and shifting trends, making it suitable for complex financial data. This study aims to compare the performance of ARIMA and Prophet models in forecasting ANTM stock prices. The dataset consists of monthly closing stock prices from January 2016 to May 2025. The models are evaluated using Mean Absolute Percentage Error (MAPE), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). The results show that the ARIMA model performs better to predict PT ANTM’s traded than Prophet model, with lower MAPE, AIC, and BIC values of 7.88, 433.24, and 423.63, respectively.   PT Aneka Tambang Tbk (ANTM) adalah perusahaan pertambangan besar di Indonesia yang sahamnya aktif diperdagangkan di Bursa Efek Indonesia. Harga sahamnya dipengaruhi oleh faktor internal dan eksternal.  Memprediksi pergerakan harga saham di masa depan bagi PT ANTM dapat digunakan metode peramalan deret waktu seperti ARIMA dan Prophet. ARIMA dikenal fleksibel dan mampu memberikan hasil prediksi yang akurat. Sementara itu, Prophet unggul dalam menangani data yang memiliki nilai hilang dan tren yang berubah, menjadikannya cocok untuk data pasar keuangan yang dinamis. Penelitian ini bertujuan membandingkan performa model ARIMA dan Prophet dalam memprediksi harga saham ANTM. Data yang digunakan adalah harga penutupan saham bulanan selama periode Januari 2016 hingga Mei 2025. Perbandingan dilakukan berdasarkan nilai Mean Absolute Percentage Error (MAPE), Akaike Information Criterion (AIC), dan Bayesian Information Criterion (BIC). Hasil analisis menunjukkan bahwa model ARIMA memiliki performa lebih baik dengan nilai MAPE 7.88, AIC 433.24, dan BIC 423.63, yang lebih rendah dibandingkan dengan model Prophet.
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 Aditya Rahman Anwar, Rohimatul Ayu Sofia Christyan Tamaro Nadeak Farid, Fajri Irawan, Miranda Dimas Lailani, Ade Leander, Marleta Cornelia Lestari, Yusni Puspha Maharani, Khairunnisa Muthoharoh, Luluk Nababan, Dinda Naufal, Daffa Ahmad Nurjanah, Fitri Pitri, Rizka Putri, Lutfia Aisyah Rian Kurnia, Rian Sasongko, Dharu Cahyoaji Setiawan, Dewi Indra Siahaan, Arielva Simon Suciati, Indah Wisnubroto, M. Syamsuddin Yuliana Yuliana Yulita, Tiara