Jurnal Online Unipdu Jombang (Universitas Pesantren Tinggi Darul 'Ulum)
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IMPLEMENTASI METODE DRILL AND PRACTICE DAN MEDIA FASH CARD UNTUK MENINGKATKAN KEMAMPUAN MEMBACA KOSAKATA BAHADA INGGRIS SISWA MADRASAH IBTIDAIYAH AL-HIKMAH JATIWATES TEMBELANG JOMBANG
No full textThis research is motivated by the low ability to read student vocabulary on English subjects. The implementation of this research is to improve the ability to read the vocabulary of English students Madrasah Ibtidaiyah Al-Hikmah Jatiwates Bracelet Wave. This research using class action research was carried out as many as 2 cycles of Kurt Lewin's model. The subject of this study was Madrasah Ibtidaiyah Al-Jatiwates Jatiwates Bidders with a total of 16 students. The data analysis technique used is the Flow Model. Data collection methods use observations, interviews, tests and documentation. The pre-cycle stage for the average grade of students is 76.37 student dexterity of only 9. The cycle stage 1 on average student grades increased to 79.68 student dexterity to reach 11 students. The cycle phase 2 increased with an average of 97.37 dexterity reaching 16 students. And the presumption of student learning due diligence at the pre-cycle stage is 43.75%, whereas at the pre-cycle stage 1 68.75%, and the student's dexterity presumption at the pre-cycle stage 2 is 100%. The results of the study show that driil and practice methods and flash card media can improve the ability to read English student vocabulary in Madrasah Ibtidaiyah Al-Hikmah Jatiwates Wave Auction
Dampak Xenomania Bahasa terhadap Bisnis Kuliner di Delapan Destinasi Wisata di Daerah Sekitar Tapal Kuda Jawa Timur
Full text linkAbstrak Penelitian ini mengkaji fenomena xenomania bahasa, khususnya dalam penggunaan bahasa asing, seperti bahasa Inggris, pada menu restoran di wilayah Tapal Kuda, Jawa Timur, Indonesia. Xenomania bahasa merupakan praktik yang diadopsi oleh banyak bisnis kuliner untuk menarik wisatawan internasional dengan menawarkan pengalaman kuliner yang mudah diakses dan lebih modern. Penelitian ini berfokus pada bagaimana keterbacaan menu yang diterjemahkan memengaruhi preferensi wisatawan. Penelitian menggunakan pendekatan deskriptif kualitatif, dengan data yang dikumpulkan melalui survei yang mengevaluasi keterbacaan menu dan pilihan makanan wisatawan. Hasil penelitian menunjukkan adanya korelasi positif yang signifikan antara keterbacaan tinggi dan preferensi pelanggan, terutama dalam kategori minuman dan makanan berat. Dengan menyediakan menu yang jelas dan mudah dipahami, meskipun menggunakan kata-kata asing, restoran meningkatkan kemungkinan pelanggan memilih item tersebut. Penelitian ini menekankan pentingnya strategi penerjemahan dalam meningkatkan daya saing restoran di destinasi wisata.
Abstract
This study examines the phenomenon of linguistic xenomania, particularly the use of foreign languages like English on restaurant menus in the Tapal Kuda area of East Java, Indonesia. Many culinary businesses adopt this practice to appeal to international tourists by providing accessible and modern dining experiences. The research focuses on the impact of menu readability on tourist preferences. Using a qualitative descriptive method, data was gathered through surveys assessing the clarity of menu items and the resulting food choices made by tourists. The results show a strong positive link between clear, readable menus and customer preferences, especially for drinks and main dishes. Even with foreign language elements, menus that are easy to understand increase the likelihood of customers choosing those items. This study highlights the crucial role of translation strategies in improving the competitiveness of restaurants in tourist destinations.
Keywords: linguistic xenomania, menu readability, tourist preference
Classification of Betel Leaf Diseases Based on Convolutional Neural Network to Increase Production Herbal Spice Materials
No full textTraditional medicine is the practice of utilizing medicinal plants to treat various illnesses, passed down from generation to generation. In Indonesia, there are various traditional medicines, one of which is using green betel leaves. One part of the green betel plant that is commonly attacked by pests is the leaf. The Convolutional Neural Network (CNN) method is a very common method used for image classification because this method produces the highest accuracy in classification and pattern recognition. This research uses data totaling 4000 images which are divided into four classes: healthy green betel leaves, anthracnose green betel leaves, bacterial spot betel leaves, and healthy red betel leaves. Detecting the disease type facilitates farmers in acknowledging the necessary measures required to provide treatment. Therefore, this study utilizes the benefits of the CNN approach, specifically its capability to conduct precise object detection and classification in image data, to minimize the widespread of disease. The CNN architectures implemented are DenseNet201, EfficientNetB3V2, InceptionResNetV2, MobileNetV2 and XceptionResnet50V2. Based on our research, the InceptionResNetV2 model achieved the highest performance with an accuracy of 86.0%, loss of 0.3880, and ROC of 98.0%. In the other hand, the MobileNetV2 and EfficientNetV2B3 models suffered from overfitting and underfitting and the models failed to classify betel leaf diseases
Klasifikasi Ulasan Pelanggan pada Aplikasi MitraShopee Menggunakan Metode Support Vector Machine dan Naïve Bayes
No full textPenelitian ini bertujuan untuk menghasilkan ulasan pelanggan aplikasi MitraSHopee ke dalam tiga kategori, yaitu, kritik, saran, dan pertanyaan, menggunakan algoritma Naïve Bayes dan Support Vector Machine (SVM). Sebanyak 6.000 ulasan diambil dari Google Play Store dan diproses melalui tahapan pembersihan data seperti case folding, tokenizing, stopword removal, dan normalisasi menggunakan Python di Google Colab. Data yang telah dibersihkan kemudian dianalisis dengan TF-IDF dan dilabeli berdasarkan pendekatan berbasis atiran. Hasil evaluasi menujukkan bahwa algoritma SVM memiliki performa lebih unggul dengan akurasi sebesar 81%, sedangkan Naibe Bayes mencatatkan akurasi 56%. SVM menunjukkan keseimbangan yang baik antara precision dan recall, khususnya dalam mendeteksi ulasan saran dan pertanyaan. Sementara itu, Naïve Bayes cenderung bias terhadap satu kelas dan kurang mampu mengenali kritik dengan baik. Penelitian ini menunjukkan pentingnya pemilihan algoritma dan tahap preprocessing dalam klasifikasi teks. Hasil yang diperoleh diharapkan dapat membantu pengembang aplikasi dalam memahami kebutuhan pengguna secara lebih cepat dan tepat sasaran melalui system klasifikasi otomatis
Pengenalan Gambar Untuk Pelatihan Bicara Bagi Anak Dengan Speech Delay Menggunakan Convolutional Neural Network
No full textPenelitian ini bertujuan untuk membangun dan mengevaluasi model Convolutional Neural Network (CNN) dalam mengklasifikasikan gambar coretan tangan berupa angka dan bentuk bangun datar. Dataset yang digunakan merupakan gabungan dari MNIST dan Google QuickDraw, dengan total 16 kelas. Model CNN yang dikembangkan terdiri dari empat blok konvolusi dan dua fully connected layer, dilatih selama 30 epoch menggunakan teknik regularisasi dan normalisasi untuk meningkatkan performa generalisasi. Hasil pelatihan menunjukkan akurasi validasi tertinggi sebesar 98,61% dan loss minimum sebesar 0,1185. Evaluasi menggunakan metrik precision, recall, dan f1-score menunjukkan rata-rata nilai sebesar 97,71%, 98,22%, dan 98,75%. Model ini menunjukkan kinerja tinggi pada kelas angka maupun bentuk bangun datar, meskipun terdapat tantangan pada bentuk visual yang kompleks dan serupa. Dengan akurasi dan konsistensi klasifikasi yang tinggi, model CNN ini dinilai layak sebagai komponen utama dalam sistem bantu pembelajaran visual untuk anak dengan keterlambatan bicara (speech delay)
IMPLEMENTASI METODE EKSPERIMEN DAN MEDIA HUJAN BUATAN UNTUK MENINGKATKAN HASIL BELAJAR IPA DI MADRASAH IBTIDAIYAH SALAFIYAH SYAFI’IYAH BANDUNG III DIWEK JOMBANG
No full textThe purpose of this study is to improve the learning outcomes of grade V students in science subjects of water cycle material by applying experimental methods and artificial rain media. This research was carried out because student learning outcomes were still low. This study used classroom action research which was carried out as many as two cycles according to Kurt Lewin's model. The subjects of this study were 27 students of class V MISS Bandung III Diwek Jombang. The data collection methods used are observation, interviews, tests and documentation. The results showed that in the pre-cycle stage, 10 students completed with an average of 64.8 and a completion percentage of 37% At the stage of cycle I student learning outcomes increased with an average score of 74.1 with a percentage of completeness of 59% and cycle II increased with a percentage of 89% with an average of 85.7. The results showed that learning using experimental methods and artificial rain media could improve the learning outcomes of grade V students MISS Bandung III Diwek Jombang
CONTRIBUTING FACTORS AND INTERVENTION EFFORTS FOR DIABETES MELLITUS AMONG ADOLESCENTS: A LITERATURE REVIEW
No full textThe rising prevalence of Type 2 Diabetes Mellitus (T2DM) among adolescents has become a significant global public health concern, including in Indonesia. This literature review aims to explore the key contributing factors and effective countermeasures in addressing adolescent diabetes. Drawing upon 35 peer-reviewed studies published between 2018 and 2024, the review identifies obesity, physical inactivity, unhealthy dietary habits, and genetic predisposition as the most prominent risk factors. Additionally, emerging contributors such as psychosocial stress and sleep disturbances are gaining attention in recent studies. The review also examines various preventive and control efforts, including school-based health programs, family involvement, digital health interventions, and national policies such as GERMAS. While many intervention strategies show promise, implementation gaps and lack of culturally adapted models remain challenges, particularly in low- and middle-income countries. Compared to previous studies, this review highlights a shift toward more holistic and biopsychosocial approaches to T2DM prevention in youth. It concludes that multifaceted interventions tailored to adolescents' specific sociocultural contexts are essential for effective and sustainable outcomes. Further research is needed to expand local evidence and evaluate long-term impacts of ongoing health programs
APRS and SSTV Technology for Audiovisual Data Transmission in Internet Blank Spot Areas to Increase the Effectiveness of SAR Activities
No full textVolcanic eruptions can be detected through several warning signs. The Indonesian National Disaster Management Agency (BNPB) reported that between 2010 and 2021, Indonesia experienced 156 volcanic eruptions. The most recent occurred in 2021 when Mount Semeru erupted, forcing 10,395 people to evacuate, injuring 104, and causing 51 fatalities. The BNPB often experiences problems in carrying out mitigation, evacuation, rehabilitation, and reconstruction in disaster areas. On average, the search and evacuation process for victims takes about 3-7 days, so the probability of finding disaster victims is only about 50%. The proposed solution is a combination of radio transmission with Auto Packet Reporting System (APRS) technology as a medium for determining evacuation locations and Slow-Scan Television (SSTV) as a medium for transmitting audio and images of disaster sites, called Radio All-in-One (RAIONE). Using the Prototype method, this research has been tested for about 7 months with continuous improvements. The results show that the maximum distance covered is approximately 20 km with a minimum central antenna height of 7-10 meters, which increases the time effectiveness of SAR operations. The probability of finding survivors in a disaster increases to 75%, and SAR operations speed up to 1-2 days because of acceleration in the determination of search and evacuation locations in the Blank Spot Areas, reaching 91.30%
A Fuzzy Control System for Performance Optimization in Wireless Sensor Networks
No full textWireless Sensor Networks (WSNs) play a vital role in numerous domains such as environmental monitoring, healthcare, industrial automation, and smart city infrastructures. Despite their growing significance, WSNs face persistent challenges, including limited energy resources, high data loss, network instability, and latency issues. To address these concerns, this study explores the integration of fuzzy logic to optimize WSN performance under uncertain and dynamic conditions. A fuzzy logic-based control system was designed to adaptively regulate key parameters, such as node energy, packet loss, and connectivity. Simulations were conducted with varying node densities (100, 200, and 300 nodes) to assess the effectiveness of the approach. The results revealed notable improvements: energy consumption was reduced by up to 0.65%, network lifetime extended by up to 0.28%, packet delivery ratio increased by up to 3.10%, and average latency decreased by up to 43.8%. These outcomes underscore the potential of fuzzy logic to enhance the adaptability, efficiency, and reliability of WSNs, offering a practical and scalable solution for performance optimization in real-world deployments
The Effect of GeoGebra-Assisted E-Modules on Students' Ability to Solve Problems Mathematically
Full text linkThis research implements a posttest-only control group experimental design to examine the impact of a GeoGebra-integrated e-module on students' mathematical problem-solving abilities. A sample of 68 eighth-grade learners at SMP Negeri 57 Palembang participated in the study, divided into experimental and control groups. The experimental group utilized an interactive e-module featuring GeoGebra software integration, while the control group received conventional instruction. The results showed that students who used the e-module achieved significantly better outcomes in problem recognition, strategy development, implementation of solutions, and outcome assessment. The mean posttest results obtained by the experimental group were higher than those of the control group. A t-test confirmed a statistically significant difference between the two groups. Based on statistical analysis, integrating GeoGebra within a structured digital module demonstrates potential to strengthen students' conceptual understanding, engagement, and problem-solving skills, especially in learning straight-line equations. Furthermore, the approach promotes higher-order thinking and learner autonomy, offering a promising strategy for improving mathematics instruction at the junior high school level.This research implements a posttest-only control group experimental design to examine the impact of a GeoGebra-integrated e-module on students' mathematical problem-solving abilities. A sample of 68 eighth-grade learners at SMP Negeri 57 Palembang participated in the study, divided into experimental and control groups. The experimental group utilized an interactive e-module featuring GeoGebra software integration, while the control group received conventional instruction. The results showed that students who used the e-module achieved significantly better outcomes in problem recognition, strategy development, implementation of solutions, and outcome assessment. The mean posttest results obtained by the experimental group were higher than those of the control group. A t-test confirmed a statistically significant difference between the two groups. Based on statistical analysis, integrating GeoGebra within a structured digital module demonstrates potential to strengthen students' conceptual understanding, engagement, and problem-solving skills, especially in learning straight-line equations. Furthermore, the approach promotes higher-order thinking and learner autonomy, offering a promising strategy for improving mathematics instruction at the junior high school level