Abstract:Autisme merupakan kondisi perkembangan saraf yang dapat berpengaruh pada berbagai ranah perkembangan anak, termasuk komunikasi, interaksi sosial, perhatian, dan keterampilan motorik. Salah satu kemampuan yang dapat mengalami…
lami hambatan adalah motorik halus, yaitu kemampuan menggunakan otot-otot kecil secara terkoordinasi untuk melakukan aktivitas yang memerlukan ketepatan gerak. Hambatan tersebut dapat memengaruhi keterlibatan anak dalam kegiatan belajar maupun aktivitas sehari-hari. Penelitian ini bertujuan menganalisis perubahan kemampuan motorik halus seorang siswa autis di Islamic Kids House Buah Hati Kita Jember setelah memperoleh stimulasi melalui permainan pipe block. Penelitian menggunakan pendekatan kuantitatif dengan metode Single Subject Research (SSR) dan desain A-B. Subjek penelitian merupakan siswa autis TK A berinisial NA yang menunjukkan hambatan pada keterampilan motorik halus. Data dikumpulkan melalui observasi dan tes perbuatan, kemudian dianalisis secara deskriptif menggunakan grafik serta analisis dalam kondisi dan antar kondisi. Persentase kemampuan pada fase baseline berturut-turut adalah 40%, 45%, dan 55%. Pada fase intervensi, persentase meningkat menjadi 65%, 75%, 80%, 80%, dan 85%. Analisis antar kondisi memperlihatkan perubahan level sebesar 10% dengan persentase overlap 0%. Pola tersebut menunjukkan adanya peningkatan performa setelah intervensi diberikan. Dengan demikian, pipe block berpotensi menjadi media pembelajaran konkret yang dapat digunakan untuk memberikan latihan motorik halus pada siswa autis.
Autism is a neurodevelopmental condition that can affect various domains of a child's development, including communication, social interaction, attention, and motor skills. One of the abilities that may be impaired is fine motor skills, which refer to the coordinated use of small muscles to perform activities requiring precise movements. These difficulties can affect children's participation in learning activities and daily life tasks. This study aimed to analyze changes in the fine motor skills of an autistic student at Islamic Kids House Buah Hati Kita Jember after receiving stimulation through pipe block play activities. The study employed a quantitative approach using the Single Subject Research (SSR) method with an A-B design. The research subject was an autistic kindergarten student (TK A), identified by the initials NA, who demonstrated difficulties in fine motor skills. Data were collected through observation and performance tests, then analyzed descriptively using graphical analysis, within-condition analysis, and between-condition analysis. The percentages of performance during the baseline phase were 40%, 45%, and 55%, respectively. During the intervention phase, the percentages increased to 65%, 75%, 80%, 80%, and 85%. Between-condition analysis showed a level change of 10% with an overlap percentage of 0%. This pattern indicates an improvement in performance following the intervention. Therefore, pipe blocks have the potential to serve as a concrete instructional medium for providing fine motor skill training to autistic students.
Abstract:This research aims to analyse the process and impact of restructuring at PT Adhya Tirta Batam (ATB). This research uses qualitative research. Data collection is done through direct observation and documentation. The analysis…
ysis technique used to analyse the process and impact of restructuring that occurred at PT Adhya Tirta Batam (ATB) was Strength, Weakness, Threat, and Threat (SWOT) analysis. The results of the analysis of the process and impact of restructuring that occurred at PT Adhya Tirta Batam (ATB) are: 1) Process Analysis: Since ATB underwent restructuring, ATB has continued to improve its performance to become one of the best water companies in Indonesia. Even though ATB's performance is not only recognised nationally but also internationally, 2) Impact Analysis: After restructuring, the quality and performance of the company have improved. Since 2010, ATB's water leakage rate has continued to decline, with an average of 15.28% per year. This is supported by the company's innovation strategy related to the use of appropriate technology to detect water pipes owned by ATB.
Abstract:Green betel leaves contain compounds that can treat vaginal discharge, especially those caused by bacteria. To determine the effect of green betel leaf extract on the growth of Bacteroides in vaginal secretion cultures,…
this study used a quantitative experimental method, where vaginal secretion samples were taken from the subjects and then tested quantitatively. There were two data collection techniques used in this study: using a questionnaire and testing vaginal secretion samples with green betel leaf extract. The test for using was diffusion (paper disc) for the antibacterial test. The media used was MHA. The positive control used was a blank disc, the extract concentration series used were 20%, 25% and 30%. The diameter of the inhibition zone was observed and then analyzed. The results of the t-test analysis showed that the significance value was 0.00 <0.05, so H0 was rejected Ha was accepted or it could be concluded that there was a difference in the effect of giving green betel leaf extract on the growth of Bacteroides in vaginal secretion culture. Antibacterial activity test of green betel leaves (Piper betle L) against Bacteroides bacteria that cause Bacterial Vaginosis, namely resistant at a concentration of 20%, intermediate at a concentration of 25% and sensitive at a concentration of 30%. Green betel leaf extract (Piper betle L) has inhibitory power against Bacteroides bacteria that cause Bacterial Vaginosis. Can increase the concentration series of methanol extract of green betel leaves, find the minimum bactericidal concentration (MBC) and the maximum inhibitory concentration (MIC) of green betel leaf extract against Bacteroides bacteria that cause Bacterial Vaginosis
Abstract:A cooling machine or commonly called an Air Conditioner (AC) is a system of auxiliary machines that can create cool temperatures in certain rooms so that workers on ships feel comfortable. Cooling machines are used to regulate…
gulate and manage air quality which are includes air circulation, air conditioning, air hygiene standards and air purification. There are several main components in the cooling machine, namely the Compressor, Condenser, Expansion Valve, Evaporator. Each of these important components has different functions and ways of operating, but are connected to each other so that the cooling machine can function. The purpose of the author making this final project is to determine the performance of the cooling system, factors that reduce the performance of the cooling machine, as well as repair and maintenance efforts so that the cooling machine can return to normal operation. This research was conducted on the MV. Vancouver by using a qualitative approach. The author conducted direct observations as well as in-depth interviews and documentation studies when conducting research. The results of the author's research found that the main factor causing the decline in the performance of the cooling machine was the appearance of sparks on the evaporator and coil pipe caused by interference with the filter dryer so that it inhibits heat absorption and the evaporator cannot evaporate the refrigerant optimally. The impact caused by the less than optimal operation of the cooling machine in the Engine Room Workshop MV. Vancouver was an unstable room temperature making the crew feel uncomfortable while working or resting.
Abstract:Kreativitas berperan penting dalam memfasilitasi ekspresi diri dan memberikan kepuasan pribadi, terutama bagi anak-anak yang merupakan masa depan bangsa. Pengembangan kreativitas sejak usia dini sangatlah penting. Penelitian…
tian ini fokus pada pelatihan pembuatan buket dari kawat bulu (pipe cleaner) di SD Negeri Sumurboto, yang bertujuan untuk meningkatkan keterampilan kreativitas siswa kelas 6. Metode pelatihan yang digunakan bersifat kualitatif, dengan pendekatan terstruktur dan terorganisir, dilakukan pada 25 Juli 2024. Pelatihan ini melibatkan 56 siswa yang dibagi menjadi 14 kelompok, dengan setiap kelompok didampingi oleh anggota tim pengabdian. Pelatihan ini bertujuan untuk memperkenalkan teknik pembuatan buket bunga dari kawat bulu sebagai sarana peningkatan kreativitas. Alat dan bahan yang digunakan meliputi kertas cellophane, floral foam, tusuk sate, kawat bulu, selotip, pita, gunting, dan lem tembak. Proses pelatihan mencakup pembuatan pola bunga dari kawat bulu, perakitan dengan tusuk sate, wrapping dengan kertas cellophane, dan penyelesaian dengan pita. Hasil pelatihan menunjukkan bahwa siswa menunjukkan antusiasme dan keterlibatan yang tinggi, kemampuan mereka dalam merancang dan menciptakan karya seni meningkat. Program ini tidak hanya mendorong kreativitas siswa tetapi juga menyediakan bekal keterampilan yang dapat diterapkan dalam peluang usaha di masa depan. Melalui pelatihan ini, diharapkan siswa dapat menyalurkan ide-ide kreatif mereka secara efektif dan memperoleh keterampilan baru yang berharga untuk perkembangan pribadi dan profesional mereka.
Abstract:Abstract: Manual meeting documentation risks losing important information due to cognitive fatigue. Although automated summarization models have evolved, integrated end-to-end systems for Indonesian spoken language remain…
n highly limited. This study aims to design and evaluate an end-to-end automated meeting summarization architecture that directly integrates Automatic Speech Recognition (ASR) via OpenAI Whisper for transcription and the IndoT5 language model for abstractive summarization. IndoT5 was fine-tuned using a dataset of 486 Indonesian spoken language transcript pairs. Testing was conducted on a CPU infrastructure using MP4, MP3, and WAV formats. Results show the optimal fine-tuning configuration significantly improved accuracy, achieving ROUGE-1 (0.4167), ROUGE-2 (0.1973), and ROUGE-L (0.2701) scores. Computationally, the system achieved a Real-Time Factor below 1, processing data faster than the actual recording duration. Conclusively, integrating Whisper and IndoT5 shows potential in producing coherent meeting summaries with lightweight computational overhead, making it viable for local infrastructure implementation to ensure data privacy.
Keywords: abstractive summarization; ASR; end-to-end pipeline; IndoT5; real-time factor
Abstrak: Dokumentasi rapat manual rentan menghilangkan informasi penting akibat keterbatasan kognitif. Meskipun model peringkas otomatis telah berkembang, implementasi sistem terintegrasi (end-to-end) khusus percakapan lisan berbahasa Indonesia masih sangat terbatas. Penelitian ini bertujuan merancang dan mengevaluasi arsitektur peringkas rapat otomatis end-to-end yang mengintegrasikan langsung Automatic Speech Recognition (ASR) melalui OpenAI Whisper untuk transkripsi dan model bahasa IndoT5 untuk peringkasan abstraktif. Adaptasi domain dilakukan melalui fine-tuning IndoT5 menggunakan 486 pasang dataset transkrip lisan berbahasa Indonesia. Pengujian pada infrastruktur CPU menggunakan format MP4, MP3, dan WAV. Hasil pengujian menunjukkan konfigurasi fine-tuning optimal berhasil meningkatkan akurasi, dengan skor ROUGE-1 (0,4167), ROUGE-2 (0,1973), dan ROUGE-L (0,2701). Sistem mendemonstrasikan efisiensi komputasi dengan nilai Real-Time Factor di bawah 1, mengindikasikan waktu pemrosesan lebih cepat dari durasi rekaman asli. Kesimpulannya, integrasi Whisper dan IndoT5 menunjukkan potensi dalam menghasilkan ringkasan yang koheren dengan beban komputasi ringan, sehingga layak diimplementasikan pada infrastruktur lokal organisasi untuk menjaga privasi data.
Kata kunci: ASR; end-to-end pipeline; IndoT5; peringkasan abstraktif; real-time factor
Abstract:YouTube has become a major platform for public discourse in Indonesia, yet large-scale sentiment analysis of its comments remains challenging due to dynamic content, informal language, and limited labeled data. This study…
y proposes a Selenium–IndoBERT pipeline for sentiment analysis of Indonesian YouTube comments using a pseudo-labeling approach. Data were collected from ten YouTube videos discussing the One Piece flag phenomenon, yielding 10,842 comments after preprocessing. Selenium was employed to extract comments from dynamic pages, while IndoBERT was fine-tuned on a small manually labeled dataset and used to generate pseudo-labels for unlabeled data. Model performance was evaluated using probabilistic metrics, including Coverage, Expected Calibration Error (ECE), and Brier Score. At a confidence threshold of 0.75, 78.5% of comments received pseudo-labels, with an ECE of 0.095 and a Brier Score of 0.174. Manual validation showed substantial agreement with human annotations (Fleiss’ kappa = 0.72). The results indicate that the proposed pipeline enables scalable and reliable sentiment analysis with minimal manual annotation.
Abstract:Abstract: The growing intensity of cyber attacks, marked by rapid, large-scale, automated, and adaptive execution, requires analytical methods that represent the diversity of network environments, including variations in…
target platforms such as IoT, traditional networks, and hybrid infrastructures. This study compares machine learning models for cyber attack classification under heterogeneous environmental conditions and formulates a conceptual optimization framework based on model performance. Four publicly available benchmark datasets were used, namely UNB CIC IoT 2023, UNB CIC IDS-2018, UNSW-NB15, and a Kaggle cyber security attacks dataset, comprising approximately 40,000 to over 3.6 million records and 25 to 80 features across IoT, conventional, and mixed network environments. Random Forest, XGBoost, Multilayer Perceptron, and Transformer were implemented within a unified pipeline involving preprocessing, feature selection, and Bayesian Optimization-based hyperparameter tuning. All models achieved F1-score and Cohen's Kappa above 96%, with XGBoost performing best (97.80%, 97.26%), followed by Random Forest (97.78%, 96.96%) and Transformer (97.44%, 96.82%), while MLP scored lowest (96.74%, 96.00%), a gap below one percentage point. Confusion matrix analysis revealed persistent misclassification in minority and overlapping attack classes, informing a proposed adaptive cyber attack simulation optimization framework.
Keywords: cyber attacks; optimization; machine learning; environmental variability.
Abstrak: Meningkatnya intensitas serangan siber yang berlangsung cepat, masif, otomatis, dan adaptif menuntut pendekatan analitis yang merepresentasikan keragaman lingkungan jaringan, termasuk perbedaan karakteristik platform sasaran seperti Internet of Things (IoT), jaringan konvensional, dan infrastruktur hibrida. Penelitian ini membandingkan model machine learning untuk klasifikasi serangan siber pada kondisi lingkungan heterogen, sekaligus menyusun kerangka optimasi konseptual berdasarkan performa model. Empat dataset benchmark publik digunakan, yaitu UNB CIC IoT 2023, UNB CIC IDS-2018, UNSW-NB15, serta dataset Kaggle cyber security attacks, dengan jumlah data berkisar 40.000 hingga lebih dari 3,6 juta rekaman dan 25 sampai 80 fitur, mewakili lingkungan IoT, konvensional, dan campuran. Random Forest, XGBoost, Multilayer Perceptron, dan Transformer diimplementasikan melalui pipeline terpadu mencakup pra-pemrosesan, seleksi fitur, dan optimasi hyperparameter berbasis Bayesian Optimization. Seluruh model mencapai F1-score dan Cohen's Kappa di atas 96%, dengan XGBoost menunjukkan performa terbaik (97,80%, 97,26%), diikuti Random Forest (97,78%, 96,96%) dan Transformer (97,44%, 96,82%), sementara MLP mencatat skor terendah (96,74%, 96,00%), dengan selisih kurang dari satu poin persentase. Analisis confusion matrix mengungkap misklasifikasi yang konsisten pada kelas minoritas dan serangan dengan karakteristik serupa, yang menjadi dasar kerangka optimasi simulasi serangan siber adaptif yang diusulkan.
Kata kunci: serangan siber; optimasi; machine learning; variabilitas lingkungan
Abstract:Abstract: Accurate bone age estimation is essential for monitoring pediatric growth, diagnosing endocrine disorders, and supporting clinical decision-making. Although deep learning has improved prediction accuracy, limited…
ed studies have systematically examined how increasing model depth affects performance and reliability. This study evaluates the effectiveness of progressively deeper convolutional neural networks, specifically EfficientNet variants B0 to B5, for bone age estimation from hand radiographs. Experiments were conducted using 12,611 hand X-ray images from the RSNA Pediatric Bone Age Challenge dataset on Kaggle. To ensure fair comparison, all models were trained using a unified and consistent training pipeline. Model performance was evaluated using Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Concordance Correlation Coefficient (CCC), and Pearson correlation coefficient. The results show a consistent improvement in prediction accuracy as model depth increases. Among the evaluated models, EfficientNet-B5 achieved the best performance, with an MAE of 21.5 months, MAPE of 6.23%, CCC of 0.9148, and Pearson’s r of 0.9203. These findings confirm that model scaling plays a critical role in enhancing prediction robustness and clinical reliability. Future work should emphasize external validation across diverse populations and incorporate interpretability techniques, such as Grad-CAM, to improve clinical transparency and trust.
Keywords: bone age prediction; deep learning; model evaluation; clinical validation
Abstrak: Estimasi usia tulang yang akurat sangat penting untuk memantau pertumbuhan anak, mendiagnosis gangguan endokrin, dan mendukung pengambilan keputusan klinis. Meskipun pembelajaran mendalam telah meningkatkan akurasi prediksi, studi yang secara sistematis meneliti bagaimana peningkatan kedalaman model memengaruhi kinerja dan keandalan masih terbatas. Studi ini mengevaluasi efektivitas jaringan saraf konvolusional yang semakin dalam, khususnya varian EfficientNet B0 hingga B5, untuk estimasi usia tulang dari radiografi tangan. Eksperimen dilakukan menggunakan 12.611 gambar sinar-X tangan dari dataset RSNA Pediatric Bone Age Challenge di Kaggle. Untuk memastikan perbandingan yang adil, semua model dilatih menggunakan alur pelatihan yang terpadu dan konsisten. Kinerja model dievaluasi menggunakan Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Concordance Correlation Coefficient (CCC), dan koefisien korelasi Pearson. Hasil menunjukkan peningkatan yang konsisten dalam akurasi prediksi seiring dengan peningkatan kedalaman model. Di antara model yang dievaluasi, EfficientNet-B5 mencapai kinerja terbaik, dengan MAE sebesar 21,5 bulan, MAPE sebesar 6,23%, CCC sebesar 0,9148, dan Pearson’s r sebesar 0,9203. Temuan ini menegaskan bahwa penskalaan model memainkan peran penting dalam meningkatkan optimasi prediksi dan keandalan klinis. Penelitian selanjutnya dapat menekankan validasi eksternal di berbagai populasi dan menggabungkan teknik interpretasi, seperti Grad-CAM, untuk meningkatkan transparansi dan kepercayaan klinis.
Kata kunci: prediksi usia tulang; deep learning; evaluasi model; validasi klinis
Abstract:Abstract: SMP Muhammadiyah 5 Samarinda still relies on manual evaluation with limited data analysis tools in predicting student academic achievement. This study aims develop a system for predicting the learning achievement…
nt of students at SMP Muhammadiyah 5 Samarinda using the Naive Bayes classification method. The dataset used consists of 192 student exam scores covering academic scores, attendance, parents’ education and income, and living conditions as independent variables, while the dependent variable is the achievement label (achieved or not achieved). The preprocessing stage includes label normalization, feature selection, and median imputation to handle missing data. The dataset was divided into 75% training data and 25%. The model was implemented as a pipeline consisting of a median imputer and a Gaussian Naive Bayes classifier. The evaluation results showed that the model achieved an accuracy of 79.2%, with a perfect recall value (1.00) in the high-achieving class and (0.64) in the low-achieving class. This shows that the model is quite effective in identifying high-achieving students. The trained model was then integrated into a Flask-based web application, which enables online predictions through a simple form interface, facilitating contextual interpretation. This system is expected to assist in educational decision-making by helping teachers identify students’ achievement levels early on and design more targeted learning interventions.
Keywords: academic performance; educational data mining; naive bayes; prediction system; student achievement
Abstrak: SMP Muhammadiyah 5 Samarinda masih bergantung pada evaluasi manual dengan alat analisis data terbatas dalam melakukan prediksi prestasi akademik siswa. Penelitian ini bertujuan mengembangkan sistem prediksi prestasi belajar siswa SMP Muhammadiyah 5 Samarinda menggunakan metode klasifikasi Naive Bayes. Dataset yang digunakan terdiri atas 192 data nilai ujian siswa yang mencakup skor akademik, kehadiran, pendidikan dan pendapatan orang tua, serta kondisi tempat tinggal sebagai variabel independen, sedangkan variabel dependen berupa label prestasi (berprestasi atau tidak berprestasi). Tahap preprocessing meliputi normalisasi label, seleksi fitur, serta imputasi median untuk menangani data yang hilang. Dataset dibagi menjadi 75% data latih dan 25%. Model diimplementasikan dalam bentuk pipeline yang terdiri atas median imputer dan Gaussian Naive Bayes classifier. Hasil evaluasi menunjukkan bahwa model mencapai akurasi sebesar 79,2%, dengan nilai recall sempurna (1,00) pada kelas berprestasi dan lebih rendah (0,64) pada kelas tidak berprestasi. Hal ini menunjukkan bahwa model cukup efektif dalam mengidentifikasi siswa berprestasi. Model yang telah dilatih kemudian diintegrasikan ke dalam aplikasi web berbasis Flask, yang memungkinkan prediksi secara daring melalui antarmuka formulir sederhana untuk mendukung interpretasi kontekstual. Sistem ini diharapkan dapat membantu untuk pengambilan keputusan dalam pendidikan dengan membantu guru mengidentifikasi tingkat prestasi siswa sejak dini dan merancang intervensi pembelajaran yang lebih terarah.
Kata kunci: prestasi akademik; penambangan data Pendidikan; naive bayes; sistem prediksi; prestasi siswa