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DIRJA NUR ILHAM
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SULIWA: Jurnal Multidisiplin Teknik, Sains, Pendidikan dan Teknologi
Published by Lembaga Kajian Pembangunan Pertanian dan Lingkungan
ISSN : 30646103     EISSN : 30646103     DOI : https://doi.org/10.62671/suliwa.v1i1.12
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering Engineering
SULIWA: Jurnal Multidisiplin Teknik, Sains, Pendidikan dan Teknologi adalah jurnal multidisiplin, peer-review, dan akses terbuka yang menyediakan platform untuk menghasilkan penelitian asli berkualitas tinggi, Ulasan, Surat, dan laporan kasus dalam ilmu alam, sosial, terapan, formal, seni, dan semua bidang terkait lainnya. Tujuan kami adalah untuk meningkatkan distribusi cepat ide dan hasil penelitian baru dan memungkinkan para peneliti untuk menciptakan pengetahuan, studi, dan inovasi baru yang akan membantu sebagai alat referensi untuk masa depan. Artikel yang dikirim ke SULIWA tidak boleh dipublikasikan di tempat lain. Naskah harus mengikuti pedoman penulis yang disediakan oleh SULIWA dan harus ditinjau dan diedit. SULIWA diterbitkan tiga kali dalam satu tahun, yaitu pada bulan Maret, Juli dan Nopember.
Arjuna Subject : Umum - Umum
Articles 64 Documents
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PENGEMBANGAN MODEL PREDIKSI PELUANG JUARA TURNAMEN SEPAK BOLA ASEAN CHAMPIONSHIP 2026 (AFF CUP) MENGGUNAKAN EXTREME GRADIENT BOOSTING (XGBOOST) BERDASARKAN STATISTIK PERFORMA TIM NASIONAL Mohamad Iqbal Ulumando
SULIWA: Jurnal Multidisiplin Teknik, Sains, Pendidikan dan Teknologi Vol. 3 No. 2 (2026): SULIWA: Jurnal Multidisiplin Teknik, Sains, Pendidikan dan Teknologi, Juli 2026
Publisher : LEMBAGA KAJIAN PEMBANGUNAN PERTANIAN DAN LINGKUNGAN (LKPPL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62671/suliwa.v3i2.311

Abstract

The ASEAN Championship is the most prestigious football competition in Southeast Asia, bringing together the best national teams from ASEAN Football Federation (AFF) member countries. Due to the high level of competition among participants, predicting the likelihood of winning cannot rely solely on FIFA Rankings; it requires an analysis that considers various statistical indicators of national team performance. This study aims to develop a prediction model for the 2026 ASEAN Championship winner using the Extreme Gradient Boosting (XGBoost) algorithm, based on national team performance statistics. The variables utilized include FIFA Rankings, performance in the last 10 matches, goals scored, goals conceded, and head-to-head records. Research data were obtained from FIFA, the ASEAN Football Federation (AFF), and various international football statistical databases. Prior to model training, the data underwent preprocessing stages—including cleaning, normalization, and encoding—and the dataset was split into 80% for training and 20% for testing. Model evaluation was conducted using the Confusion Matrix, Accuracy, Precision, Recall, F1-Score, and Receiver Operating Characteristic–Area Under Curve (ROC-AUC). Evaluation results demonstrate that the XGBoost model performs exceptionally well, achieving Accuracy, Precision, Recall, and F1-Score values ​​of 87.50% each, along with an AUC value of 0.94. Based on the predictions, Indonesia holds the highest probability of winning at 24.35%, followed by Vietnam (20.18%) and Thailand (16.42%). The findings indicate that the Extreme Gradient Boosting (XGBoost) algorithm is capable of generating an accurate, objective, and comprehensive prediction model for analyzing championship prospects based on a combination of national team performance indicators, thereby offering an effective approach for predictive analysis in football competitions.
KLASIFIKASI CALON TOP SCORER TURNAMEN SEPAK BOLA ASEAN CHAMPIONSHIP 2026 (AFF CUP) MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE BERDASARKAN DATA STATISTIK PERFORMA PEMAIN Mohamad Iqbal Ulumando; Orry Adrian Mokola; Risni Stefani
SULIWA: Jurnal Multidisiplin Teknik, Sains, Pendidikan dan Teknologi Vol. 3 No. 3 (2026): SULIWA: Jurnal Multidisiplin Teknik, Sains, Pendidikan dan Teknologi, Nopember
Publisher : LEMBAGA KAJIAN PEMBANGUNAN PERTANIAN DAN LINGKUNGAN (LKPPL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62671/suliwa.v3i3.312

Abstract

The 2026 ASEAN Championship (AFF Cup) is a prestigious football tournament in Southeast Asia that consistently captures public interest, particularly regarding the identification of potential top scorers. Historically, identifying these candidates has relied on subjective observation rather than data-driven analysis. This study aims to apply the Support Vector Machine (SVM) algorithm to classify potential top scorers based on performance statistics—specifically goals, shots, and assists—recorded over the past year. Secondary data was utilized, comprising five potential players from each of the ten participating nations, resulting in a dataset of 50 players. The research process involved data collection, preprocessing, Min-Max normalization, class label assignment, splitting the data into training and testing sets (80:20 ratio), applying the SVM algorithm, and evaluating the model using a confusion matrix, accuracy, precision, recall, and F1-score. The results indicate that the SVM algorithm successfully classified 17 players into Class A (High Potential to be Top Scorer), 17 players into Class B (Potential to be Top Scorer), and 16 players into Class C (Low Potential to be Top Scorer). Model evaluation yielded an accuracy of 60.00%, demonstrating the SVM algorithm's ability to objectively classify potential top scorers based on performance statistics. This study is expected to serve as a reference for developing machine learning-based football performance analysis and to support the identification of potential top scorers prior to the tournament.
KLASIFIKASI KANDIDAT MOST VALUABLE PLAYER (MVP) PADA TURNAMEN SEPAK BOLA ASEAN CHAMPIONSHIP 2026 MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE (SVM) BERDASARKAN STATISTIK PERFORMA PEMAIN Mohamad Iqbal Ulumando
SULIWA: Jurnal Multidisiplin Teknik, Sains, Pendidikan dan Teknologi Vol. 3 No. 3 (2026): SULIWA: Jurnal Multidisiplin Teknik, Sains, Pendidikan dan Teknologi, Nopember
Publisher : LEMBAGA KAJIAN PEMBANGUNAN PERTANIAN DAN LINGKUNGAN (LKPPL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62671/suliwa.v3i3.313

Abstract

The Most Valuable Player (MVP) award recognizes the player who makes the most significant contribution during a football tournament. MVP candidates are typically selected based on subjective assessments that consider various aspects of player performance. Therefore, this study aims to apply the Support Vector Machine (SVM) algorithm to classify Most Valuable Player (MVP) candidates for the 2026 ASEAN Championship football tournament based on player performance statistics. The study utilizes a dataset comprising 20 players from 10 participating nations, incorporating six variables: Goals, Assists, Key Passes, Successful Dribbles, Tackles, and Interceptions. The research process involves data collection, data preprocessing, normalization using the Min-Max method, model construction using the Support Vector Machine (SVM) algorithm, and model evaluation using a Confusion Matrix, Accuracy, Precision, Recall, and F1-Score. The results demonstrate that the Support Vector Machine (SVM) algorithm can classify players into three categories: Class A (High Potential to be MVP), Class B (Potential to be MVP), and Class C (Low Potential to be MVP). Model evaluation results show an Accuracy of 90.00%, Precision of 89.47%, Recall of 90.00%, and F1-Score of 89.73%, indicating strong model performance in classifying MVP candidates based on performance statistics. This study demonstrates that the Support Vector Machine (SVM) algorithm serves as an effective machine learning-based approach to support the objective, data-driven identification of Most Valuable Player (MVP) candidates.
PENGEMBANGAN E-MODUL EKOLOGI BERBASIS ALEF EDUCATION UNTUK MENINGKATKAN KEPEDULIAN LINGKUNGAN PADA MATA PELAJARAN EKONOMI KELAS X DI SMA MUHAMMADIYAH 09 KUALUH HULU Siti Jaisah; Dirgantara Wicaksono
SULIWA: Jurnal Multidisiplin Teknik, Sains, Pendidikan dan Teknologi Vol. 3 No. 2 (2026): SULIWA: Jurnal Multidisiplin Teknik, Sains, Pendidikan dan Teknologi, Juli 2026
Publisher : LEMBAGA KAJIAN PEMBANGUNAN PERTANIAN DAN LINGKUNGAN (LKPPL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62671/suliwa.v3i2.316

Abstract

The rapid advancement of digital technology has transformed learning into a more interactive and student-centered process. Environmental issues such as deforestation, river pollution, and natural resource exploitation require educational institutions to integrate environmental education into various subjects, including economics. This study aimed to develop an ecology e-module based on Alef Education to support economics learning. The study employed the Research and Development (R&D) method using the ADDIE model. Data were collected through observations, interviews, expert validation questionnaires, and student response questionnaires. The findings indicate that the developed e-module is highly feasible and practical for classroom use. The module enhances students' understanding of economics while fostering environmental awareness through contextual learning and interactive digital media. The developed product can support the implementation of the Merdeka Curriculum and promote sustainable learning.

Page 7 of 7 | Total Record : 64

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