Abstract:Pandemic conditions have caused several elements in society, including the economy, health and especially education to change. So that shifts and innovations are needed, one of which is teaching techniques in Sunday school…
ol to help teachers convey the Word of God in class. Such as the use of props that can help teachers convey stories to children in class. The use of props that involve IT or audio visual media-based technology such as videos, televisions, slides is expected to attract children’s attention so that they can understand the essence of the story. The teaching of audio-visual media is inspired by the teaching method that the Lord Jesus applied when teaching the crowds and His disciples, “Look at the birds of the sky who neither plant nor reap nor gather into barns” (Matthew 6:34). In teaching, the Lord Jesus often used natural media as a way for Him to convey His Word. This research discusses the role of audio visual media used by teachers in delivering various topics. This research uses a qualitative approach with descriptive methods. Based on this research, it is concluded that the role of audio-visual media is so great in helping Sunday school teachers in teaching to be easily accepted and understood by children and recorded more easily in children’s memory.
Abstract:The cognitive development of early childhood requires appropriate stimulation, one of which is through color recognition. Color block media serves as an educational tool that not only introduces various colors but also familiarizes…
amiliarizes children with geometric shapes, numerical concepts, and trains their thinking and memory skills. This study aims to implement color block media as an innovative learning method to effectively improve early childhood abilities in color recognition. Early childhood is a stage of exploration, where the learning process must be concrete, engaging, and enjoyable. Color block media combines visual and manipulative approaches that can foster curiosity and active involvement in the learning process. This learning-through-play activity encourages children to naturally identify, differentiate, and name colors. The study used a quantitative method with a pretest-posttest design to determine the effectiveness of the media. The research was conducted at KB Adduriyah 3 on October 29, 2024, and data analysis was performed using a t-test through SPSS 18 for Windows. The results showed a significance value (2-tailed) of 0.00. Since this value is smaller than the significance level (α = 0.05), the null hypothesis is rejected, and the alternative hypothesis is accepted. The results of the study demonstrate that color block media has a significant impact on improving children's color recognition skills. Additionally, the media also enhances children's active participation in learning activities. Therefore, color block media is highly recommended as a creative and effective learning strategy for educators and parents in supporting the cognitive development of early childhood.
Abstract:Abstract: This community service aims to empower people who are members of the Yasayan Senyum Ibu Indonesia (YSII), so that they dare to appear to do public speaking. Empowerment needs to be done, considering the number…
of Foundation activities that require public speaking skills. The empowerment method used in this service has three stages. The first stage is to introduce and teach participants, how to find material or material to be delivered in public speaking, using the internet. After the material is obtained, they are asked to arrange the sentences that will be delivered in public speaking, then practice them one by one. The second method is to teach and practice the participants to hear a lot while memorizing the words that have been heard, to be memorized and repeated. This method is the basic method of public speaking, which relies a lot on memory, based on what is heard, then memorized. After memorizing, recited in public speaking activities. The third method is one by one the participants learn to recite flat voice intonation, as a basic technique in public speaking. The results of this service are public speaking modules that will be made HKI, popular articles in online media, and scientific articles in public service journals.
Keywords: Public Speaking, Internet, Information Technology
Abstrak: Pengabdian ini bertujuan untuk memberdayakan masyarakat yang tergabung dalam Yasayan Senyum Ibu Indonesia (YSII), agar berani tampil untuk melakukan public speaking. Pemberdayaan ini perlu dilakukan, mengingat banyaknya kegiatan Yayasan yang memerlukan keterampilan public speaking. Metode pemberdayaan yang digunakan dalam pengabdian ini ada tiga tahap. Tahap pertama, adalah mengenalkan dan mengajarkan kepada para peserta, cara mencari bahan atau materi yang akan disampaikan dalam public speaking, dengan menggunakan internet. Setelah bahan di dapatkan, mereka diminta untuk menyusun kalimat yang akan disampaikan di dalam public speaking, kemudian mempraktikannya satu-persatu. Metode kedua adalah mengajarkan dan mempraktikan kepada para peserta untuk banyak mendengar sekaligus menghafalkan kata-kata yang telah didengar, untuk dihapalkan dan diulang kembali. Metode ini adalah metode dasar public speaking, yang banyak mengandalkan daya ingat, berdasarkan dari apa yang didengar, kemudian dihapalkan. Setelah hapal, dilafalkan dalam kegiatan public speaking. Metode ketiga adalah satu persatu peserta belajar melafalkan intonasi suara datar, sebagai teknik dasar dalam public speaking. Hasil dari kegiatan pengabdian ini adalah modul public speaking yang akan dibuat HKI, artikel popular di media online, adan artikel ilmiah di jurnal pengadian masyarakat.
Kata Kunci: Public Speaking, Internet, Teknologi informasi
Abstract:Abstract: Existing IoT anomaly detection studies have achieved high classification performance, but most focus on accuracy and F1-score without explicitly controlling the false positive rate (FPR). In addition, many approaches…
oaches rely on a single detection perspective, limiting their operational reliability. To address this gap, this study proposes a hybrid anomaly detection framework integrating Long Short-Term Memory (LSTM), Shannon entropy, and autoencoder reconstruction error. Shannon entropy is incorporated as an additional feature, while LSTM and the autoencoder capture temporal and reconstruction characteristics. The resulting hybrid representation is processed by a constraint-based threshold selection mechanism that enforces FPR . Experiments on the TON-IoT and Edge-IIoTset datasets achieved average F1-scores of 0.9250 and 0.9934, while maintaining average FPR values of 0.0091 and 0.0714, respectively. Analysis of entropy distributions showed consistent differences between normal and anomalous traffic across both datasets, indicating that Shannon entropy provides discriminative information for anomaly detection. These results demonstrate strong detection performance with controlled false alarms, while ablation studies confirm the significant contribution of Shannon entropy to overall model performance.
Keywords: false positive rate; hybrid deep learning; Internet of Things; network anomaly detection; Shannon entropy
Abstrak: Penelitian deteksi anomali Internet of Things (IoT) telah menunjukkan performa klasifikasi yang tinggi, namun sebagian besar masih berfokus pada accuracy dan F1-score tanpa mengendalikan false positive rate (FPR) secara eksplisit. Selain itu, banyak pendekatan hanya memanfaatkan satu perspektif deteksi sehingga reliabilitas operasionalnya masih terbatas. Untuk mengatasi kesenjangan tersebut, penelitian ini mengusulkan kerangka deteksi anomali hybrid yang mengintegrasikan Long Short-Term Memory (LSTM), Shannon entropy, dan autoencoder reconstruction error. Shannon entropy digunakan sebagai fitur tambahan, sedangkan LSTM dan autoencoder menangkap karakteristik temporal dan deviasi rekonstruksi. Representasi hybrid yang dihasilkan kemudian diproses melalui mekanisme constraint-based threshold selection dengan batas FPR . Hasil pengujian pada dataset TON-IoT dan Edge-IIoTset menghasilkan F1-score rata-rata sebesar 0,9250 dan 0,9934, dengan FPR rata-rata sebesar 0,0091 dan 0,0714. Perbedaan nilai entropy yang konsisten antara trafik normal dan anomali pada kedua dataset menunjukkan bahwa Shannon entropy menyediakan informasi diskriminatif untuk deteksi anomali. Hasil tersebut menunjukkan performa deteksi yang kuat dengan false alarm yang terkendali, sementara studi ablasi mengonfirmasi kontribusi signifikan Shannon entropy terhadap performa model.
Kata kunci: deteksi anomali jaringan; false positive rate; hybrid deep learning; Internet of Things; Shannon entropy
Abstract:Abstract: The capital market plays an important role in describing the economic conditions of a country, and the IHSG is used as the main indicator to observe the movement of all stocks on the Indonesia Stock Exchange. Because…
ecause stock data is volatile and non-linear, the forecasting process becomes challenging, requiring methods that can capture historical patterns more accurately. This study aims to predict IHSG movements using the Long Short-Term Memory (LSTM) model to generate stable short-term predictions. Historical IHSG data was used to train the model, and accuracy was evaluated using Mean Squared Error (MSE). The results show that the model obtained an MSE 6784.0207, RMSE 82.3652 and MAPE 0.88%, indicating a relatively low prediction error rate. The visualization shows that the model's predictions are very close to the actual data, and the 60-day forecasting results show a potential increase in the IHSG of 1.05%. Thus, the LSTM model is capable of providing fairly accurate IHSG predictions and can be a useful tool for investors in analyzing short-term market movements.
Keywords: forecasting; JCI; long short term memory
Abstrak: Pasar modal memiliki peran penting dalam menggambarkan kondisi ekonomi suatu negara, dan IHSG digunakan sebagai indikator utama untuk melihat pergerakan seluruh saham di Bursa Efek Indonesia. Karena data saham bersifat fluktuatif dan tidak linear, proses peramalan menjadi tantangan, sehingga dibutuhkan metode yang mampu menangkap pola historis secara lebih akurat. Penelitian ini bertujuan memprediksi pergerakan IHSG menggunakan model Long Short-Term Memory (LSTM) untuk menghasilkan prediksi jangka pendek yang stabil. Data historis IHSG digunakan untuk melatih model, kemudian akurasi dievaluasi menggunakan Mean Squared Error (MSE). Hasil penelitian menunjukkan bahwa model memperoleh nilai MSE 6784.0207, RMSE 82.3652 dan MAPE 0.88% yang menandakan tingkat kesalahan prediksi relatif rendah. Visualisasi menunjukkan bahwa prediksi model sangat mendekati data aktual, dan hasil forecasting 60 hari ke depan memperlihatkan potensi kenaikan IHSG sebesar 1,05%. Dengan demikian, model LSTM mampu memberikan prediksi IHSG yang cukup akurat dan dapat menjadi alat bantu bagi investor dalam menganalisis pergerakan pasar jangka pendek.
Kata kunci: peramalan; JCI; memori jangka pendek
Abstract:Abstract: The policy to increase the Rural and Urban Land and Building Tax (PBB-P2) in Indonesia often elicits mixed reactions from the public. Some support it because they believe it can strengthen regional fiscal capacity,…
ity, while others reject it because they are concerned that it will increase the economic burden on the community. Understanding public sentiment towards this policy is important for evaluating the effectiveness of the policy and formulating appropriate communication strategies. This study aims to analyze public sentiment towards the PBB-P2 increase policy using data uploaded on Platform X (Twitter). The data were collected through crawling with the keyword “building tax,” then processed through several preprocessing stages before classifying tweets into positive and negative sentiments. Two models were used: Support Vector Machine (SVM) and Bidirectional Long Short-Term Memory (BiLSTM). Results show that SVM outperformed BiLSTM, achieving training accuracy of 99.4% and testing accuracy of 85.9%, with accuracy 0.8595, precision 0.8536, recall 0.8595, and F1-score 0.8449. Meanwhile, BiLSTM achieved training accuracy of 86.9% and testing accuracy of 82.9%, with accuracy 0.8294, precision 0.8150, recall 0.8294, and F1-score 0.8080. These findings suggest SVM is more effective in classifying public sentiment and can support better evaluation of regional tax policies.
Keywords: sentiment analysis; PBB-P2; BiLSTM; SVM; X platform
Abstrak: Kebijakan kenaikan tarif Pajak Bumi dan Bangunan Perdesaan dan Perkotaan (PBB-P2) di In-donesia sering memunculkan beragam reaksi dari masyarakat. Sebagian mendukung karena dianggap dapat memperkuat kapasitas fiskal daerah, sementara lainnya menolak karena kha-watir menambah beban ekonomi masyarakat. Pemahaman terhadap sentimen publik atas ke-bijakan tersebut penting untuk mengevaluasi efektivitas kebijakan dan merumuskan strategi komunikasi yang tepat. Penelitian ini bertujuan menganalisis sentimen masyarakat terhadap kebijakan kenaikan PBB-P2 menggunakan data unggahan di Platform X (Twitter). Data dik-umpulkan melalui proses crawling dengan kata kunci “pajak bangunan” kemudian diproses melalui beberapa tahap preprocessing sebelum diklasifikasikan menjadi sentimen positif dan negatif. Dua model digunakan dalam penelitian ini, yaitu Support Vector Machine (SVM) dan Bidirectional Long Short-Term Memory (BiLSTM). Hasil penelitian menunjukkan bahwa SVM memiliki kinerja lebih baik dibandingkan BiLSTM, dengan akurasi pelatihan 99,4% dan akurasi pengujian 85,9%. Nilai akurasi 0,8595, precision 0,8536, recall 0,8595, dan F1-score 0,8449. Sementara itu, BiLSTM memperoleh akurasi pelatihan 86,9% dan akurasi pengujian 82,9%, dengan akurasi 0,8294, precision 0,8150; recall 0,8294; dan F1-score 0,8080. Temuan ini menunjukkan bahwa SVM lebih efektif dalam mengklasifikasikan sentimen publik serta dapat mendukung evaluasi kebijakan pajak daerah dengan lebih baik.
Kata kunci: analisis sentimen; PBB-P2; BiLSTM; SVM; platform X
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: This research is driven by the challenges faced by Universitas Lancang Kuning (UNILAK) in attracting applicants amidst intense competition, especially after the government's policy opened independent pathways to…
o State Universities (PTN) from 2022-2023, which impacted private university applicant numbers. To address this and support strategic planning, this study aims to predict the trend of prospective students applying to all study programs at UNILAK for the period 2025-2027. Two time series models were employed: ARIMA (AutoRegressive Integrated Moving Average) and LSTM (Long Short-Term Memory). Applicant data from 2019 to 2024 was used to build the model. The Augmented Dickey-Fuller (ADF) test confirmed the data's stationarity with a p-value of 0.0. ACF and PACF analyses determined the ARIMA parameters as p=1, d=1, q=1. The LSTM model was trained to capture more complex data patterns. ARIMA predictions for 2025, 2026, and 2027 are 3298.66, 3362.33, and 3371.30, respectively. LSTM predictions for the same years are 3335.64, 3476.52, and 3518.42. Evaluation using Root Mean Squared Error (RMSE) showed ARIMA (RMSE=588.72) to be more accurate than LSTM (RMSE=653.96). Nevertheless, LSTM provided a more optimistic prediction. This study concludes that ARIMA is better suited for short-term planning, while LSTM can be used for more ambitious long-term strategies.
Keywords: arima; LSTM; applicants; prediction; university
Abstrak: Penelitian ini didorong oleh tantangan Universitas Lancang Kuning (UNILAK) dalam menarik pendaftar di tengah persaingan ketat, khususnya setelah kebijakan pemerintah membuka jalur mandiri ke Perguruan Tinggi Negeri (PTN) sejak 2022-2023, yang menyebabkan penurunan jumlah pendaftar di universitas swasta. Untuk mendukung perencanaan strategis, studi ini bertujuan memprediksi tren jumlah calon mahasiswa yang mendaftar ke seluruh program studi di UNILAK untuk periode 2025-2027.Dua model deret waktu digunakan: ARIMA (AutoRegressive Integrated Moving Average) dan LSTM (Long Short-Term Memory). Data jumlah pendaftar dari 2019 hingga 2024 digunakan untuk membangun model. Uji Augmented Dickey-Fuller (ADF) menunjukkan data stasioner dengan p-value 0,0. Analisis ACF dan PACF menentukan parameter ARIMA sebagai p=1, d=1, q=1. Model LSTM dilatih untuk menangkap pola data yang lebih kompleks.Prediksi ARIMA untuk 2025, 2026, dan 2027 adalah 3298.66, 3362.33, dan 3371.30. Prediksi LSTM untuk tahun yang sama adalah 3335.64, 3476.52, dan 3518.42. Evaluasi menggunakan Root Mean Squared Error (RMSE) menunjukkan ARIMA (RMSE=588.72) lebih akurat daripada LSTM (RMSE=653.96). Meskipun demikian, LSTM memberikan prediksi yang lebih optimis. Studi ini menyimpulkan ARIMA lebih cocok untuk perencanaan jangka pendek, sementara LSTM dapat digunakan untuk strategi jangka panjang yang ambisius.
Kata kunci: arima; LSTM; pendaftar; prediksi; universitas
Abstract:Automatic text summarization is challenging research in natural language processing, aims to obtain important information quickly and precisely. There are two main approach techniques for text summary: abstractive and extractive…
tractive summary. Abstractive Summarization generates new and more natural words, but the difficulty level is higher and more challenging. In previous studies, RNN and its variants are among the most popular Seq2Seq models in text summarization. However, there are still weaknesses in saving memory; gradients are lost in long sentences so resulting in a decrease in lengthy text summaries. This research proposes a Transformer model with an Attention mechanism that can fetch important information, solve parallelization problems, and summarize long texts. The Transformer model we propose is GPT-2. GPT-2 uses decoders to predict the next word using the pre-trained model from w11wo/indo-gpt2-small, implemented on the Indosum Indonesian dataset. Evaluation assessment of the model performance using ROUGE evaluation. The study's results get an average result recall for R-1, R-2, and R-L were 0.61, 0.51, and 0.57, respectively. The summary results can paraphrase sentences, but some still use the original words from the text. Future work increase the amount of data from the dataset to improve the result of more new sentence paraphrases.
Abstract:Pendidikan Islam pada fase usia dini menuntut pendekatan yang tidak hanya bersifat kognitif-normatif, tetapi juga kinestetik-emosional guna menginternalisasi nilai-nilai ketauhidan secara holistik. Penelitian ini mengevaluasi…
luasi efektivitas penerapan Metode Kauni yang diintegrasikan melalui aktivitas Senam Asmaul Husna di TKIT Sholahuddin Al Ayyubi. Fokus utama penyelidikan adalah bagaimana stimulasi panca indera melalui modalitas Visual, Auditori, dan Kinestetik (VAK) dalam Metode Kauni mampu memperkuat fondasi spiritualitas anak. Metode penelitian yang digunakan adalah kualitatif deskriptif dengan teknik pengumpulan data melalui observasi partisipatif, wawancara mendalam dengan pendidik, serta dokumentasi kegiatan harian sekolah. Hasil penelitian menunjukkan bahwa integrasi Metode Kauni dalam senam pagi mampu menciptakan suasana belajar yang menyenangkan sesuai prinsip "Quantum Memory" yang menekankan aspek "menghafal semudah tersenyum". Penguatan nilai spiritual termanifestasi melalui peningkatan kesadaran ketuhanan, pembentukan karakter disiplin, kejujuran, dan empati sosial yang selaras dengan visi "Gema Ceria" lembaga. Penemuan ini menegaskan bahwa keterlibatan fisik yang ritmis dengan pelafalan asma Allah secara berulang-ulang memberikan jangkar memori yang kuat pada anak usia dini, sehingga nilai-nilai spiritualitas tidak hanya dihafal secara lisan namun juga tercermin dalam perilaku keseharian.