Abstract:This study examines food security, food self-sufficiency, and self-sufficiency in South Sulawesi in 2024, focusing on the province's main food commodities, distribution issues, and the impact of government policies. The…
research analyzes the gap between food production and demand, identifying key commodities such as rice, corn, and soybeans. By evaluating the availability, accessibility, and affordability of these staple foods, the study investigates how local agricultural practices, infrastructure, and external factors like climate change and market volatility affect food security in the region. The findings reveal significant challenges in meeting the demand for rice and soybeans, despite the province's ability to achieve a surplus in corn production. The methodology employed in this study involves both qualitative and quantitative approaches. Primary data was gathered through field surveys, interviews with local farmers, agricultural experts, and policymakers, as well as secondary data from government reports, agricultural statistics, and market analyses. A comparative analysis was conducted to examine trends in food production, consumption patterns, and price fluctuations over the past five years. The study also incorporated geographic information system (GIS) mapping to assess the distribution of agricultural resources and the effectiveness of existing infrastructure in connecting farmers to markets. The findings suggest that despite the province's agricultural potential, South Sulawesi continues to face challenges in achieving full food self-sufficiency. The study recommends a multifaceted approach to addressing food insecurity and achieving food self-sufficiency, including improving rural infrastructure, adopting modern agricultural technologies, and diversifying food crops.
Abstract:The examination of hemoglobin levels in the elderly is crucial as iron deficiency or anemia is often found in this age group. Various methods of hemoglobin examination are used in healthcare facilities. This study aims to…
o identify the differences in hemoglobin levels using the Dirui BCC-3600 and Quick Check devices in the elderly population at Puskesmas Biromaru. This research is descriptive-comparative in nature. The sample consisted of 30 patients undergoing hemoglobin examination at the Puskesmas Biromaru laboratory between March 29th and April 30th, 2023. The sampling method used was accidental sampling, with venous blood samples from 30 respondents. The results of the study showed that the average hemoglobin level using the Dirui BCC-3600 device was 12.86 g/dL, while with the Quick Check device it was 11.77 g/dL. Normality testing was conducted using the Shapiro-Wilk test, which yielded a p-value of 0.023. A p-value < 0.05 indicates non-normal data distribution, thus followed by the Mann-Whitney test. The Mann-Whitney test resulted in a significant value (p) = 0.011. In conclusion, there is a significant difference in hemoglobin levels using the Dirui BCC-3600 and Quick Check devices in the elderly.
Abstract:Injected contraception is the most contraception method chosen by Indonesian women. There are 2 types of injected contraception, 3 monthly injecion which consists of progesterone only and 1 monthly injection consists of…
f a combination of oesterogen-progesterone. One of its side effect is increase weight, which is usually distressing for women due to the increase risk of suffering from many diseases such as heart attack, type-2 diabetes mellitus, sleep apnea, certain cancer, osteoarthritis, and asthma. The study aim was to identify the difference weight gain occurence between 3 monthly and 1 monthly injected contraception users in Muara Village- Suranenggala District. This research method uses comparative analytic studies using cross sectional resulting research which measured in interval scale. The sample is all 3 months KB injecting participants as much as 30 respondents and 1 month KB injections as much as 30 respondents. The data is analysis using t-test. The result showed a ρ value of 0,005 which meant there was a difference in weight gain occurence between the users of 3 monthly and 1 monthly injected contraception. The 3 monthly injection had been proven increasing the incidence of weight gain compared to the 1 monthly one. It was suggested that midwives should give proper counselling regarding to side effect of increasing weight gain during the use of 3 monthly contraceptive injection to be aware of by the users. Further investigation on other influencing factors of weight gain among contraceptive injection users.
Abstract:This study was motivated by the low level of professional competence among elementary school teachers despite the fact that most teachers have obtained certification and adequate academic qualifications. Curriculum changes,…
es, advances in educational technology, and the demands of 21st-century learning require teachers to possess adaptive, reflective, and collaborative professional competencies. The research problem focused on the influence of reflective practice and best practice on the professional competence of teachers at A-accredited public elementary schools in Cilegon City. The study employed a quantitative approach with a causal-comparative design involving 105 teachers selected through proportionate stratified random sampling. Data were collected using Likert-scale questionnaires and analyzed through multiple linear regression using EViews 12. The results revealed that reflective practice had a positive and significant effect on teachers’ professional competence, with a t-value of 4.697 and a significance value of 0.000. Best practice also demonstrated a positive and significant effect, with a t-value of 5.065 and a significance value of 0.000. Simultaneous testing indicated that reflective practice and best practice jointly had a significant effect on teachers’ professional competence, with an F-value of 43.72 and a coefficient of determination of 52.2%. The findings confirm that reflective and collaborative cultures are capable of improving teachers’ professional quality continuously through learning evaluation, sharing teaching experiences, and developing innovative instructional strategies. The novelty of this study lies in the simultaneous examination of reflective practice and best practice through a quantitative approach within the context of A-accredited public elementary schools in Cilegon City. The findings also demonstrate that best practice exerts a greater influence than reflective practice on the professional competence of elementary school teachers.
Abstract:Upah merupakan hak dasar pekerja yang wajib dilindungi, baik dalam perspektif hukum positif maupun hukum Islam. Penelitian ini bertujuan mengkaji konsep ijarah bi al-’amal dalam hukum Islam sebagai landasan filosofis perlindungan…
erlindungan upah pekerja, serta membandingkannya dengan pengaturan pengupahan dalam Undang-Undang Ketenagakerjaan di Indonesia. Metode yang digunakan adalah yuridis normatif dengan pendekatan perundang-undangan (statute approach), pendekatan konseptual, dan pendekatan perbandingan hukum (comparative approach). Bahan hukum diperoleh melalui studi kepustakaan atas Al-Qur’an, hadis, kitab-kitab fikih muamalah, peraturan perundang-undangan, serta jurnal hukum mutakhir, yang selanjutnya dianalisis secara deskriptif-kualitatif. Hasil penelitian menunjukkan bahwa konsep ijarah bi al-’amal menempatkan upah sebagai hak yang harus dipenuhi secara adil, proporsional, dan tepat waktu, sebagaimana ditegaskan dalam Al-Qur’an Surah At-Talaq ayat 6, Surah Al-Qashash ayat 26-27, dan hadis riwayat Ibnu Majah. Prinsip tersebut memiliki keselarasan nilai dengan semangat Undang-Undang Nomor 13 Tahun 2003 tentang Ketenagakerjaan sebagaimana diubah dengan Undang-Undang Nomor 6 Tahun 2023 tentang Cipta Kerja serta Peraturan Pemerintah Nomor 36 Tahun 2021 tentang Pengupahan, meskipun keduanya berbeda pada aspek sumber otoritas, mekanisme penetapan upah minimum, dan bentuk sanksi. Penelitian ini merekomendasikan agar nilai-nilai ijarah bi al-’amal, khususnya prinsip maqashid syariah, diintegrasikan sebagai basis etik dalam pembaruan kebijakan pengupahan nasional, terutama untuk melindungi pekerja pada sektor informal dan ekonomi gig yang belum sepenuhnya terjangkau oleh regulasi ketenagakerjaan formal.
Wages are a fundamental right of workers that must be protected, both from the perspective of positive law and Islamic law. This study aims to examine the concept of ijarah bi al-’amal in Islamic law as a philosophical foundation for wage protection, and to compare it with wage regulations under Indonesia’s Manpower Law. The results show that ijarah bi al-’amal positions wages as a right that must be fulfilled fairly, proportionally, and in a timely manner, as affirmed in the Quran Surah At-Talaq verse 6, Surah Al-Qashash verses 26-27, and the hadith narrated by Ibn Majah. This study employs a normative legal method with statutory, conceptual, and comparative approaches, examining legal materials from the Quran, hadith, classical fiqh literature, statutory regulations, and recent legal journals. The findings reveal that the principles of ijarah bi al-’amal align with the spirit of Law Number 13 of 2003 on Manpower as amended by Law Number 6 of 2023 on Job Creation and Government Regulation Number 36 of 2021 on Wages, although they differ in terms of the source of authority, minimum wage determination mechanisms, and forms of sanctions. This study recommends integrating the values of ijarah bi al-’amal, particularly maqashid syariah principles, as an ethical basis for reforming national wage policy, especially to protect informal and gig-economy workers not yet fully covered by formal labor regulations.
Abstract:Abstract: Query performance is a critical factor in managing large-scale databases. One of the most widely used optimization techniques is indexing. This study aims to analyze the impact of indexing on query performance…
in PostgreSQL, compare the effectiveness of B-Tree and Hash indexes, and evaluate their influence on query planner decisions. A quantitative experimental approach was employed using the TPC-H benchmark dataset at scale factors SF0.1, SF1, and SF10. Experiments were conducted using EXPLAIN ANALYZE on exact match, range, and join queries under three conditions: without indexing, with B-Tree indexing, and with Hash indexing. The results demonstrate that indexing significantly improves query performance. For exact match queries on the SF10 dataset, execution time decreased from 93.36 ms without indexing to 0.034 ms using B-Tree and 0.045 ms using Hash indexes. For join queries, execution time was reduced from 857.77 ms to 0.180 ms using B-Tree and 0.079 ms using Hash indexes. B-Tree showed consistent performance across different query types, while Hash achieved the best results for equality-based queries. Furthermore, index usage influenced query planner decisions in selecting more efficient execution strategies. These findings indicate that appropriate index selection can substantially improve data access efficiency in PostgreSQL.
Keywords: b-tree index; hash index; PostgreSQL; query optimization; query planner
Abstrak: Performa query merupakan faktor penting dalam pengelolaan basis data berskala besar. Salah satu teknik optimasi yang umum digunakan adalah indexing. Penelitian ini bertujuan menganalisis pengaruh penggunaan indexing terhadap performa query pada PostgreSQL, membandingkan efektivitas B-Tree dan Hash index, serta mengevaluasi pengaruhnya terhadap keputusan query planner. Penelitian menggunakan metode eksperimen kuantitatif dengan dataset benchmark TPC-H pada skala SF0.1, SF1, dan SF10. Pengujian dilakukan menggunakan EXPLAIN ANALYZE pada exact match query, range query, dan join query dalam kondisi tanpa index, menggunakan B-Tree index, dan Hash index. Hasil penelitian menunjukkan bahwa indexing meningkatkan performa query secara signifikan. Pada exact match query dataset SF10, execution time menurun dari 93,36 ms tanpa index menjadi 0,034 ms menggunakan B-Tree dan 0,045 ms menggunakan Hash index. Pada join query, execution time berkurang dari 857,77 ms menjadi 0,180 ms menggunakan B-Tree dan 0,079 ms menggunakan Hash index. B-Tree menunjukkan performa yang konsisten pada berbagai jenis query, sedangkan Hash index memberikan performa terbaik pada query berbasis equality. Selain itu, penggunaan index memengaruhi keputusan query planner dalam memilih strategi eksekusi yang lebih efisien. Hasil penelitian menunjukkan bahwa pemilihan metode indexing yang tepat dapat meningkatkan efisiensi akses data pada PostgreSQL
Kata kunci: b-tree index; hash index; optimasi query; PostgreSQL; query planner
Abstract:This study aimed to compare the performance of machine learning algorithms and user experience in predicting students’ academic achievement. The research is motivated by the need for prediction systems that are not only…
y highly accurate but also easily interpretable by users. The proposed methodology involved the implementation of two algorithms, namely Decision Tree and Random Forest, using an academic dataset that included grade point average, attendance, and assessment scores. Model performance was evaluated using accuracy, precision, recall, and F1-score, while user experience was assessed through the System Usability Scale (SUS) based on a simple user interface. The findings revealed that Random Forest achieved higher predictive accuracy, whereas Decision Tree provided better interpretability and ease of understanding for users. These results indicated a trade-off between model performance and user experience, suggesting that algorithm selection should consider both aspects in order to develop an effective and user-friendly academic prediction system
Abstract:Abstract: Stunting is a chronic nutritional condition in toddlers characterized by a Height-for-Age (HFA) measurement below the standard growth threshold, necessitating early detection to prevent long-term consequences.…
This study aims to classify toddler stunting status by comparing three machine learning methods: Random Forest (RF), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM). The dataset comprises 345 toddler records from Puskesmas Indramayu (2025), including weight, height, and nutritional status based on WFA, HFA, and WFH indicators. Preprocessing steps include data cleaning, StandardScaler normalization, One-Hot Encoding for categorical features, and splitting the training and testing data with a ratio of 80:20. The comparison results are that KNN achieved the best performance with an accuracy of 71.01%, a precision of 0.69, a recall of 0.69, and an F1 score of 0.67, while RF and SVM both had an accuracy of 69.57% with F1 scores of 0.67 and 0.68, respectively. Thus, KNN demonstrated superior effectiveness in classifying the stunting status of toddlers compared to RF and SVM on this dataset.
Keywords: KNN; Random Forest; SVM; Stunting; toddlers
Abstract: Stunting adalah kondisi gizi kronis pada balita yang ditandai dengan pengukuran Tinggi Badan menurut Usia (HFA) di bawah ambang batas pertumbuhan standar, sehingga memerlukan deteksi dini untuk mencegah konsekuensi jangka panjang. Penelitian ini bertujuan untuk mengklasfikasikan status stunting pada balita dengan membandingkan tiga metode pembelajaran mesin: Random Forest (RF), K-Nearest Neighbor (KNN), dan Support Vector Machine (SVM). Kumpulan data terdiri dari 345 catatan balita dari puskesmas indramayu (2025), termaksut brat badan, tinggi badan, dan status gizi berdasarkan indicator WFA, HFA, dan WFH. Langkah-langkah prapemrosesan meliputi pembersian data, normalisasi Stand-ardScaler, One-Hot Encoding untuk fitur kategirikal, serta pembagian data pelatihan dan pengujian dengan rasio 80:20. Hasil perbadingan adalah KNN mencapai kinerja terbaik dengan akurasi 71,01%, presisi 0,69, recall 0,69, dan skor F1 sebesar 0,67, RF dan SVM keduanya memiliki akurasi 69,57% dengan skor F1 masing-masing sebesar 0,67 dan 0,68. Dengan demikian, KNN menunjukkan keefektifan yang lebih unggul dalam mengklasifikasikan status stunting balita dibandingkan dengan RF dan SVM pada da-taset ini.
Kata kunci: KNN; random forest; SVM; Stunting; Balita
Abstract:Abstract: The rapid growth of the digital music industry requires accurate music genre classification systems to enhance user experience in streaming services. This study compares a domain-specific Long Short-Term Memory…
(LSTM) network with three Large Language Models (LLMs)—HuBERT, WavLM, and WAV2Vec 2.0—for Music Genre Classification (MGC). The LSTM model was trained using Mel-spectrograms transformed from the GTZAN dataset, while the LLMs were fine-tuned using a smaller set of raw audio samples due to computational constraints. All models were tested on datasets with identical genre labels to ensure a fair evaluation. Results show that the LSTM model achieved the highest accuracy of 97.10%, outperforming HuBERT (86.00%), WavLM (83.00%), and WAV2Vec 2.0 (80.00%). The LSTM demonstrated superior generalization and stability without overfitting, while the LLMs struggled to differentiate between genres with similar acoustic characteristics. These findings indicate that general-purpose pre-trained models, although powerful, are less effective in music-specific tasks due to domain mismatch. Therefore, incorporating music-specific features and architectures remains essential for achieving higher accuracy and reliability in automatic genre classification systems.
Keywords: audio large language models; comparative deep learning; music genre classification.
Abstrak: Pertumbuhan industri musik digital yang pesat menuntut sistem klasifikasi genre musik yang akurat untuk meningkatkan pengalaman pengguna dalam layanan streaming. Penelitian ini dilatarbelakangi oleh perkembangan pesat model pembelajaran mendalam, khususnya jaringan LSTM dan model bahasa berskala besar LLM seperti HuBERT, WavLM, dan WAV2Vec 2.0, yang telah menunjukkan kemampuan representasi audio yang kuat. Tujuan penelitian ini ini membandingkan jaringan Long Short-Term Memory (LSTM) khusus domain dengan tiga model Large Language Models (LLM)—HuBERT, WavLM, dan WAV2Vec 2.0—untuk tugas Klasifikasi Genre Musik (MGC). Metode penelitian melibatkan pelatihan LSTM menggunakan data Mel-spectrogram hasil transformasi dari dataset GTZAN, sementara LLM disesuaikan (fine-tuning) menggunakan data audio mentah dalam jumlah lebih kecil karena keterbatasan komputasi. Seluruh model diuji pada dataset dengan label genre yang sama untuk memastikan evaluasi yang adil. Hasil penelitian menunjukkan bahwa model LSTM mencapai akurasi tertinggi sebesar 97,10%, sedangkan model HuBERT, WavLM, dan WAV2Vec 2.0 masing-masing memperoleh 86,00%, 83,00%, dan 80,00%. Model LSTM menunjukkan kemampuan generalisasi yang lebih baik tanpa overfitting, sedangkan model LLM cenderung kesulitan membedakan genre dengan karakteristik akustik yang mirip. Kesimpulan penelitian ini adalah ketidaksesuaian domain secara signifikan membatasi performa model umum saat diterapkan pada tugas berbasis musik. Oleh karena itu, penggunaan fitur dan arsitektur khusus musik sangat penting dalam membangun sistem klasifikasi genre yang lebih akurat.
Kata kunci: klasifikasi genre musik; model bahasa besar; perbandingan pembelajaran mendalam.
Abstract:Abstract: The rapid growth of the cosmetics industry on e-commerce platforms has intensified competition, creating a critical need for effective, data-driven marketing strategies. This study aims to conduct a comparative…
analysis of machine learning algorithms to predict the sales categories (High, Medium, Low) of cosmetic products on the Tokopedia marketplace. Four classification models; Random Forest, XGBoost, Logistic Regression, and Naive Bayes were trained and evaluated on data collected via web scraping. The methodology incorporates the Synthetic Minority Over-sampling Technique (SMOTE) to address significant class imbalance and GridSearchCV for hyperparameter optimization to ensure a fair and robust comparison. The experimental results conclusively show that the Random Forest model achieved the best performance, yielding the highest F1-Score Macro Average of 0.75 and an accuracy of 85.3%. The superior model was subsequently implemented in a simple recommendation system to simulate optimal discount strategies, demonstrating its practical utility in providing actionable insights for business decisions.
Keywords: classification; comparative analysis; machine learning; sales prediction; SMOTE
Abstrak: Pertumbuhan pesat industri kosmetik pada platform e-commerce telah membuat persaingan ketat, sehingga menciptakan kebutuhan krusial akan strategi pemasaran yang efektif dan berbasis data. Penelitian ini bertujuan untuk melakukan analisis komparatif terhadap algoritma machine learning untuk memprediksi kategori penjualan (Tinggi, Sedang, Rendah) produk kosmetik di marketplace Tokopedia. Empat model klasifikasi, yaitu Random Forest, XGBoost, Regresi Logistik, dan Naive Bayes, dilatih dan dievaluasi menggunakan data yang dikumpulkan melalui web scraping. Metodologi penelitian ini menerapkan Synthetic Minority Over-sampling Technique (SMOTE) untuk mengatasi ketidakseimbangan kelas yang signifikan dan GridSearchCV untuk optimisasi hyperparameter guna memastikan perbandingan yang adil. Hasil eksperimen menunjukkan bahwa model Random Forest mencapai performa terbaik, dengan menghasilkan F1-Score Macro Average tertinggi sebesar 0,75 dan akurasi 85,3%. Model unggul ini kemudian diimplementasikan dalam sebuah sistem rekomendasi sederhana untuk menyimulasikan strategi diskon yang optimal, yang menunjukkan kegunaan praktisnya dalam memberikan wawasan yang dapat ditindaklanjuti untuk pengambilan keputusan bisnis.
Kata kunci: analisis komparatif; klasifikasi; machine learning; prediksi penjualan; SMOTE