Abstract:Abstract: The literacy and numeracy achievements of elementary school students in Indonesia still face various challenges, despite showing an upward trend based on the results of the National Assessment. One cause is the…
implementation of conventional and less interactive learning models. This community service activity aims to develop and train the use of the interactive Learning Management System (LMS) kelaspetualang.com integrated with Scratch-based educational games to support literacy and numeracy learning at SD Negeri 067240 Medan. The implementation method used an interactive workshop based on practice-based learning that included needs analysis, material development, training, implementation, mentoring, and evaluation. The activity participants consisted of 20 teachers. The evaluation was conducted using a 1–5 Likert scale questionnaire to measure understanding, interest, and effectiveness of the activity. The results of the activity showed that 18 teachers successfully developed and uploaded game-based teaching materials to the LMS, while two teachers experienced technical difficulties with the device. The evaluation results showed a very good level of understanding and enthusiasm, especially regarding interest in developing game-based learning media, although further mentoring is still needed on technical aspects. This activity has been proven to improve teachers' digital competence and literacy and has the potential to enrich literacy and numeracy learning in an innovative way.
Keywords: educational; games; scratch; literacy; numeracy
Abstrak: Capaian literasi dan numerasi siswa sekolah dasar di Indonesia masih menghadapi berbagai tantangan, meskipun menunjukkan tren peningkatan berdasarkan hasil Asesmen Nasional. Salah satu penyebabnya adalah penerapan model pembelajaran yang masih konvensional dan kurang interaktif. Kegiatan pengabdian kepada masyarakat ini bertujuan untuk mengembangkan dan melatih pemanfaatan Learning Management System (LMS) interaktif kelaspetualang.com yang terintegrasi dengan game edukatif berbasis Scratch guna mendukung pembelajaran literasi dan numerasi di SD Negeri 067240 Medan. Metode pelaksanaan menggunakan workshop interaktif berbasis practice-based learning yang meliputi analisis kebutuhan, pengembangan materi, pelatihan, implementasi, pendampingan, dan evaluasi. Peserta kegiatan terdiri atas 20 guru. Evaluasi dilakukan menggunakan kuesioner skala Likert 1–5 untuk mengukur pemahaman, minat, dan efektivitas kegiatan. Hasil kegiatan menunjukkan bahwa 18 guru berhasil mengembangkan dan mengunggah materi ajar berbasis game ke dalam LMS, sementara dua guru mengalami kendala teknis perangkat. Hasil evaluasi menunjukkan tingkat pemahaman dan antusiasme yang sangat baik, terutama pada minat pengembangan media pembelajaran berbasis game, meskipun masih diperlukan pendampingan lanjutan pada aspek teknis. Kegiatan ini terbukti meningkatkan kompetensi dan literasi digital guru serta berpotensi memperkaya pembelajaran literasi dan numerasi secara inovatif.
Kata kunci: pendidikan; permainan; Scratch; literasi; numerasi.
Abstract:Culinary is a very popular business in Indonesian society. This phenomenon has the consequence that the level of business competition becomes tighter, it is means that culinary businesses are always required to innovate…
to survive in the long run. The main focus of this community service is providing training to culinary business owners to follow dynamic consumer taste trends. This training aims to provide solutions by creating innovation and knowing the latest consumer interests. The approach used in this training is discussion and mapping of partners' challenges, then knowledge assistance and provision are provided. The results of Google Trends training are generally able to make a real contribution to business owners in achieving consumer loyalty. Apart from that, this training increases partners' knowledge in utilizing the Google Trends application to determine product focus and product marketing locations. In particular, it can foster strong self-confidence in partners in using digital business.
Keywords: digital marketing; product trends; culinary; innovation.
Abstrak: Kuliner merupakan sebuah bisnis yang sangat digemari oleh masyarakat Indonesia. Fenomena ini membawa konsekuensi pada tingkat persaingan usaha yang semakin ketat, artinya pelaku usaha kuliner dituntut untuk selalu berinovasi agar dapat bertahan dalam jangka panjang. Fokus utama pengabdian masyarakat ini adalah memberikan pelatihan kepada pemilik usaha kuliner agar dapat mengikuti tren selera konsumen yang dinamis. Pelatihan ini bertujuan untuk memberikan solusi dengan menciptakan inovasi dan mengetahui minat konsumen terkini. Pendekatan yang digunakan dalam pelatihan ini adalah diskusi dan pemetaan permasalahan mitra, kemudian diberikan pendampingan dan pembekalan pengetahuan. Hasil dari pelatihan Google Trends secara umum mampu memberikan kontribusi nyata bagi pemilik usaha dalam meraih loyalitas konsumen. Selain itu, pelatihan ini meningkatkan pengetahuan digital marketing mitra dalam menentukan fokus produk dan lokasi pemasaran produk. Secara khusus, dapat menumbuhkan rasa percaya diri yang kuat pada mitra dalam menggunakan bisnis digital.
Kata kunci: digital pemasaran; tren bisnis; kuliner; inovasi.
Abstract:Technological advances in the modern era are now becoming a trend in the lives of every individual and in various fields. One of them is QR (Quick Response) Code technology in the Android-based Heroes application. This research…
esearch explains the community service program, especially for partners at Yohanes Gabriel Catholic Elementary School located in Sidodadi, Garum District, Blitar Regency. Our service is by creating an application that contains sketches of the faces of Indonesian Heroes and their struggles both before and after independence, wrapped in short videos. The method used is face-to-face where we provide guidance to teachers in using the application we created and provide outreach to elementary school students regarding the application created. With this application which utilizes the latest technology, it has had a positive impact, especially for our partners at Yohanes Gabriel Catholic Elementary School, where the elementary school teachers can use the application we created in their teaching and learning activities..
Keywords: QR code; community service; android
Abstract:Abstract: In 2018, the number of Instagram users in Indonesia has reached 55 million users. A year earlier, Jakarta is the champ on Instagram as the most photographed place, surpassing Sao Paulo, New York and Madrid. This…
s phenomenon shows that Instagram is a social network that is trending in Indonesia right now. Trilogi Business Incubator (Inbistro) is a business incubator belonging to the Universitas Trilogi. From observations and discussions, there are still many tenants which assisted by Inbistro who do not understand digital marketing, especially with Instagram. Albeit, Instagram has become a popular social media in Indonesia, including for product promotion. This community service activity will try to answer the problem: how to increase the capacity of Inbistro tenants so that they understand the basics of Instagram marketing? Our training was designed in 3 (three) sessions which discussed: (1) the importance of using Instagram as a marketing tool for a business; (2) How to find quality, free royalty photos and videos for Instagram content; (3) How to find products and sell them on Instagram.
Keywords: Instagram marketing, Trilogi Business Incubator, Inbistro, social media, online marketing
Abstrak: Di tahun 2018, jumlah pengguna Instagram Indonesia telah mencapai 55 juta pengguna. Setahun sebelumnya, Jakarta menjadi juara di Instagram sebagai tempat yang paling banyak difoto, melewati Sao Paulo, New York dan Madrid. Fenomena ini menunjukkan bahwa Instagram adalah jejaring sosial yang sedang diminati di Indonesia saat ini. Inkubator Bisnis Trilogi (Inbistro) adalah inkubator bisnis milik Universitas Trilogi. Dari pengamatan dan diskusi, terlihat bahwa masih banyak tenant binaan Inbistro yang belum memahami tentang digital marketing, terlebih dengan Instagram. Padahal Instagram telah menjadi media sosial yang cukup populer di Indonesia, termasuk untuk promosi produk. Kegiatan pengabdian masyarakat ini akan mencoba menjawab permasalahan: bagaimana cara meningkatkan kapasitas tenant Inbistro agar mereka memahami dasar-dasar pemasaran melalui Instagram (Instagram marketing)? Pelatihan kami rancang dalam 3 (tiga) sesi yang membahas: (1) Pentingnya memanfaatkan Instagram sebagai sarana pemasaran suatu bisnis; (2) Cara mencari foto dan video berkualitas, tanpa berbayar, untuk konten Instagram; (3) Cara mencari produk dan menjualnya di Instagram.
Kata Kunci: Instagram Marketing, Inkubator Bisnis Trilogi, Inbistro, media sosial, pemasaran daring
Abstract:Abstract: The rapid growth of e-commerce in Indonesia has increased consumer interactions with digital platforms, particularly Lazada, Tokopedia, and Blibli, resulting in a large volume of customer reviews that reflect consumer…
onsumer experiences and perceptions but have not been optimally utilized in business decision-making. The main issue addressed in this study is how to process customer review data to generate meaningful information regarding consumer opinions. This research aims to apply web scraping techniques to collect customer review data and conduct sentiment analysis to identify trends in consumer opinions across the three e-commerce platforms. The dataset consists of 3,000 customer reviews, with 1,000 reviews collected from each platform, covering aspects such as shopping experience, service quality, delivery process, and customer satisfaction. The research methodology includes data collection through web scraping, text preprocessing for data cleaning and normalization, sentiment analysis using machine learning approaches, and visualization of sentiment results. The findings indicate differences in the distribution of positive, negative, and neutral sentiments across platforms, reflecting variations in consumer experiences and service strategies. These results demonstrate that sentiment analysis based on customer reviews can serve as strategic input to improve service quality, business performance, and marketing strategies in Indonesia’s e-commerce sector.
Keywords: customer reviews; digital services; e-commerce; sentiment analysis; web scarping
Abstrak: Pertumbuhan pesat e-commerce di Indonesia meningkatkan interaksi konsumen dengan platform digital, khususnya Lazada, Tokopedia, dan Blibli, yang menghasilkan ulasan pelanggan dalam jumlah besar sebagai cerminan pengalaman dan persepsi konsumen, namun belum dimanfaatkan secara optimal dalam pengambilan keputusan bisnis. Permasalahan utama penelitian ini adalah bagaimana mengolah data ulasan tersebut agar dapat memberikan informasi yang bermakna mengenai opini konsumen. Penelitian ini bertujuan menerapkan web scraping untuk mengumpulkan data ulasan pelanggan serta melakukan analisis sentimen guna mengidentifikasi tren opini konsumen pada ketiga platform e-commerce tersebut. Data yang digunakan berjumlah 3.000 ulasan pelanggan, dengan masing-masing platform diwakili oleh 1.000 ulasan yang mencakup pengalaman berbelanja, kualitas layanan, proses pengiriman, dan tingkat kepuasan pelanggan. Metode penelitian meliputi pengambilan data menggunakan web scraping, pra-pemrosesan teks untuk pembersihan dan normalisasi data, analisis sentimen dengan pendekatan pembelajaran mesin, serta visualisasi hasil sentimen. Hasil penelitian menunjukkan adanya perbedaan distribusi sentimen positif, negatif, dan netral pada setiap platform, yang mencerminkan variasi pengalaman konsumen dan strategi layanan. Temuan ini menunjukkan bahwa analisis sentimen berbasis ulasan pelanggan dapat menjadi masukan strategis untuk meningkatkan kualitas layanan, kinerja bisnis, dan strategi pemasaran e-commerce di Indonesia.
Kata kunci: customer reviews; digital services;e-commerce;sentiment analysis;web scarping
Abstract:Abstract: Mental health is an essential aspect of overall well-being, particularly for university students vulnerable to emotional strain. This study aims to identify clusters of student mental health trends using the K-Means…
Means clustering technique. The research involved 60 students from four academic programs at the Faculty of Science and Technology, selected using stratified and cluster sampling techniques. Data were collected using a modified Mental Health Inventory (MHI). The results revealed distinct commonalities among majors: the Statistics program was predominantly defined by the depressed cluster at 53.3%, while Mathematics followed at 40% within the same cluster. In contrast, Biology students predominantly fell under the neu-tral/stable cluster (66.7%), whilst Information Systems students exhibited an even distribution (33.3% per cluster) without a dominant trend. The clustering quality was evaluated using the Silhouette Coefficient, yielding a range of 0.39 to 0.60. Biology (0.60) and Statistics (0.54) exhibited a reasonable structure, but Information Systems (0.39) and Mathematics (0.34) demonstrated a deficient structure. In conclusion, K-Means effectively discerns mental health patterns, providing a data-driven basis for targeted psychological interventions in educational settings.
Keywords: biology; information systems; k-means; mathematics; mental health; silhouette coefficient; statistics
Abstrak: Kesehatan mental merupakan komponen vital dari kesejahteraan total, terutama bagi maha-siswa yang rentan terhadap stres emosional. Penelitian ini bertujuan untuk mengidentifikasi kelompok tren kesehatan mental mahasiswa melalui penerapan metode pengelompokan K-Means. Studi ini mencakup 60 mahasiswa dari empat program studi di Fakultas Sains dan Teknologi, yang dipilih melalui metode pengambilan sampel bertingkat dan kelompok. Data dikumpulkan dengan menggunakan Inventaris Kesehatan Mental (MHI) yang dimodifikasi. Temuan menunjukkan kesamaan yang jelas di antara jurusan: program studi Statistika terutama ditandai oleh kelompok depresi (53,3%), diikuti oleh Matematika dengan 40% dalam kelompok depresi. Sebaliknya, mahasiswa Biologi terutama termasuk dalam kelompok netral/stabil (66,7%), sedangkan mahasiswa Sistem Informasi memiliki distribusi yang merata (33,3% per kelompok) tanpa pola yang dominan. Kualitas pengelompokan dinilai dengan Koefisien Sil-houette, menghasilkan rentang 0,39 hingga 0,60. Biologi (0,60) dan Statistika (0,54) memiliki struktur sedang, sedangkan Sistem Informasi (0,39) dan Matematika (0,34) menunjukkan struktur yang buruk. Kesimpulannya, K-Means secara akurat mengidentifikasi tren kesehatan mental, menawarkan landasan berbasis data untuk terapi psikologis yang ditargetkan di ling-kungan pendidikan.
Kata kunci: biologi; kesehatan mental; K-Means; matematika; silhouette coefficient; sistem in-formasi; statistika
Abstract:Abstract: The inability to accurately monitor the lifespan of motorcycle batteries can lead to sudden failures, disrupt user activities, and increase maintenance costs. This issue is exacerbated by the absence of a predictive…
ctive system that can assist users and workshops in planning maintenance and managing battery inventory effectively. This study aims to develop a battery lifespan prediction model for motorcycles using the Double Moving Average (DMA) method. The model is built based on historical data from 12 motorcycle units, including usage frequency, duration, terrain conditions, and maintenance habits. Forecasting is conducted through two stages of moving averages followed by trend parameter calculations. Evaluation results show that the model has a high level of accuracy, with MAPE = 0.10, MAD = 1.68, and RMSE = 2.14, indicating very low prediction errors. In addition, DMA is also used to forecast product demand at PT Anugerah Karya Abiwara Kisaran to prevent stock shortages. The system is developed using Visual Studio 2010 and Microsoft Access and has proven effective in supporting maintenance planning and inventory control. With its high accuracy and efficiency, the results of this study provide tangible contributions to decision-making in battery maintenance and inventory management.
Keywords: battery; DMA; motorcycle; prediction.
Abstrak: Ketidakmampuan dalam memantau usia pakai aki sepeda motor secara akurat dapat menyebabkan kerusakan mendadak, mengganggu aktivitas pengguna, serta meningkatkan biaya perawatan. Permasalahan ini diperburuk oleh tidak tersedianya sistem prediktif yang membantu pengguna dan bengkel dalam merencanakan perawatan serta mengelola persediaan aki secara efisien. Penelitian ini bertujuan untuk mengembangkan model prediksi usia pemakaian aki sepeda motor dengan menggunakan metode Double Moving Average (DMA). Model dibangun berdasarkan data historis dari 12 unit sepeda motor yang mencakup frekuensi penggunaan, durasi, kondisi medan dan kebiasaan perawatan. Proses peramalan dilakukan melalui dua tahap perataan bergerak, yang kemudian diikuti dengan perhitungan parameter tren. Hasil evaluasi menunjukkan bahwa model ini memiliki tingkat akurasi yang tinggi, dengan nilai MAPE sebesar 0,10, MAD sebesar 1,68, dan RMSE sebesar 2,14, yang mengindikasikan tingkat kesalahan prediksi yang sangat rendah. Selain itu, metode DMA juga diterapkan untuk meramalkan permintaan produk pada PT Anugerah Karya Abiwara Kisaran guna mencegah terjadinya kekurangan stok. Sistem dikembangkan menggunakan Visual Studio 2010 dan Microsoft Access, serta terbukti efektif dalam mendukung perencanaan perawatan dan pengendalian persediaan. Dengan akurasi dan efisiensi yang tinggi, hasil penelitian ini memberikan kontribusi nyata dalam pengambilan keputusan terkait pemeliharaan aki dan manajemen inventori.
Kata kunci: baterai; DMA; prediksi; sepeda motor.
Abstract:Abstract: This study aims to develop a Deep Learning-based Traffic Flow Detector to automatically and accurately observe traffic flow. Conventional traffic observation is often conducted manually or via CCTV, but it is prone…
rone to human error and difficult to use for real-time trend analysis. In this study, the YOLOv4 method is used to detect four types of vehicles (cars, motorcycles, buses, trucks). To continuously track vehicle movement and address occlusion issues, the Deep SORT algorithm is implemented. The YOLOv4 model used is a pre-trained model and was tested on seven CCTV video recordings obtained from the official website of the Pekanbaru City Transportation Department. The system was implemented on a limited device, the Nvidia Jetson Nano, as a simulation of direct CCTV integration. Test results showed a highest precision of 98%, but the maximum accuracy achieved was only 26%. This low accuracy is influenced by several factors, including video resolution, detection model quality, and lighting conditions. Nevertheless, the system demonstrates potential to support future traffic management and engineering decisions but still requires further optimization, including improving video resolution and quality, retraining the model with a more representative local dataset, using lighter and more accurate detection models, and optimizing the tracking algorithm.
Keywords: deep learning; deepsort; NVIDIA Jetson NANO; traffic flow; YOLOv4
Abstrak: Penelitian ini bertujuan mengembangkan Traffic Flow Detector berbasis Deep Learning untuk mengobservasi arus lalu lintas secara otomatis dan akurat. Observasi lalu lintas konvensional sering dilakukan secara manual atau melalui CCTV, namun rentan terhadap human error dan sulit digunakan untuk menganalisis tren secara real-time. Pada penelitian ini digunakan metode YOLOv4 untuk mendeteksi empat jenis kendaraan (mobil, motor, bus, truk). Untuk melacak pergerakan kendaraan secara berkelanjutan dan mengatasi masalah occlusion, digunakan algoritma Deep SORT. Model YOLOv4 yang digunakan merupakan pre-trained model dan diujikan pada tujuh rekaman video CCTV yang diambil dari situs resmi Dinas Perhubungan Kota Pekanbaru. Sistem ini diimplementasikan pada perangkat terbatas Nvidia Jetson Nano sebagai simulasi penerapan langsung pada CCTV. Hasil pengujian menunjukkan presisi tertinggi mencapai 98%, namun akurasi tertingginya hanya sebesar 26%. Rendahnya akurasi dipengaruhi oleh beberapa faktor seperti resolusi video, kualitas model deteksi, serta kondisi pencahayaan. Meski demikian, sistem ini menunjukkan potensi untuk membantu pengambilan keputusan dalam manajemen dan rekayasa lalu lintas di masa depan, namun masih membutuhkan optimasi lebih lanjut, seperti peningkatan kualitas video input, pelatihan ulang model dengan dataset lokal, penggunaan model deteksi yang lebih ringan dan akurat serta pengoptimalan algoritma pelacakan.
Kata kunci: deep learning deepsort; Nvidia Jetson Nano; traffic flow; YOLOv4
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:Face recognition has become a common thing used in the field of surveillance and security in computer technology and image devices. This study aims to identify the usefulness of a person's face on 3 test images. This study…
dy examines the methods of cropping techniques, image enhancement through intensity measurement, and histogram analysis to improve the contrast and distribution of image intensity. In addition, the Viola-Jones algorithm is used to detect key facial features such as eyes, nose, and mouth. The results of the analysis are then applied in the feature evaluation stage, where usually between facial features are applied to measure the ratio of facial proportions. Furthermore, the comparison of proportional ratios of several images was analyzed using bar graphs and line graphs to evaluate the trend and stability of facial proportions. The results showed the best ratio stability with a smaller variation of the on-off ratio of image 2 which is 0.4762 pixels to 0.4983 pixels. Image 2 is the most ideal for face measurement systems based on geometric ratios because it provides more consistent and visible results.