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PELATIHAN MICROSOFT OFFICE DI SMK N 2 BALIGE Susanti Samosir, Hernawati; Hutapea, Oppir; Manurung, Philippians; Ray Hutauruk, Andi; Sinambela, Amsal; Partumpoan Siahaan, Kevin; Sinaga, Hasan; Albi Pulo S, Samuel; Tomfie Bukit, Kenan; Panjaitan, Claudia
J-Dinamika : Jurnal Pengabdian Masyarakat Vol 9 No 3 (2024): Desember
Publisher : Politeknik Negeri Jember

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Basic Ms Office knowledge is one of the most general and commonly used in industry or companies from the lowest level to the hight type of industry. However, in Toba area itself, mentioned as SMK N 2 Balige which located in Balige, North Sumatra which all students do not familiar with Ms Office usage. There are no special subjects that can support other subjects according to the major, namely basic Microsoft Office science. As mentioned by the headmaster of SMK N 2 Balige that the graduations are only 30 person(10%) got accepted in industry every year. Seeing this issue, IT Del has a responsibility to increased student performance which will be accepted in industry during the graduation timeline every year. IT Del as an educational institution operating in the field of technology can of course provide assistance in the form of training that will help these students to have the skills of Ms. Offices that are needed in the industrial company. The training carried out at the school is Ms Word, Ms Excel, Ms Power Point. Each is given a module that describes the real work in the industrial world so that students are expected to be able to adapt to the learning modules provided. These three materials are taught in parallel and alternately so that all students have the opportunity to discuss the training module as a whole. The assessment technique is seen from the student's speed in working on the example questions given and being able to solve them well and correctly. The average ability of students in working on Ms Word questions is 59.33, Ms Excel 75.78 and Ms Power Point 76.44. In this training, 3 students were obtained with the highest scores from all students, namely 270, 265 and 260. From the results of the training carried out, it can be seen that the students of SMK N 2 Balige were enthusiastic in participating in Ms. Office.

Association Rules Menggunakan Algoritma FP-Growth Untuk Tata Letak Di Koperasi IT DEL Hutapea, Oppir
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 5 No 1 (2024): Oktober 2024 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v5i1.9393

Koperasi IT Del is located in IT Del campus that sells a range of office and school supplies, drinks, and snacks. For almost 20 years, the Koperasi IT Del has recorded and arranged goods manually, disregarding the needs or purchasing patterns of its clientele. The owners frequently fail to see how consumer behavior affects the sales of the cooperatives they oversee. Owners may find it easier to access their consumers' associative nature if they can identify user behavior and purchasing patterns. FP-Growth is a method that solves item layout issues by utilizing transaction or historical data that is already accessible. With a minimum support value of 24% and a minimum confidence level of 60%, this investigation yielded 47 association rules. 24 attributes that would be used from transaction history data that had already been verified were acquired from the data transformation results that were performed. The end consequence is that each association rule forms a close-knit product arrangement or position based on the item set that is commonly purchased. The Cimory UHT Matcha 20 ml box is positioned next to the 42 g Roma Coconut Cream Chocolate product (48.1%), then the My-Gell Blue Pen (47.3%), and finally the Standard AE7 Red Pen (48.1%), which is positioned next to the 42 g Roma Coconut Cream Chocolate product (48.1%), and finally next to the Nescafe Original 240 ml (47.3%).

Analisis Pengaruh Fasilitas Kesehatan terhadap Kematian COVID-19 dengan Integrasi Data, Prediksi, dan MongoDB Hutapea, Oppir; Pasaribu, Laura Vegawani; Sidabutar, Yenita; Hutajulu, Kevin; Telaumbanua, Emalia Putri; Sinaga, Syahrial
Jurnal Rekayasa Teknologi Informasi (JURTI) Vol 9, No 2 (2025): Jurnal Rekayasa Teknologi Informasi (JURTI)
Publisher : Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/jurti.v9i2.20126

Pandemi COVID-19 menimbulkan tantangan besar bagi sistem kesehatan di Indonesia, dengan lebih dari 155.000 kematian hingga tahun 2022. Penelitian ini bertujuan untuk menganalisis pengaruh ketersediaan fasilitas kesehatan terhadap tingkat kematian akibat COVID-19 di Indonesia dengan pendekatan data science. Data yang digunakan mencakup informasi fasilitas kesehatan dari Dinas Kesehatan dan Kementerian Kesehatan, serta data epidemiologi COVID-19, yang diintegrasikan berdasarkan wilayah administratif. Proses analisis mencakup tahapan pra-pemrosesan data, penyimpanan menggunakan MongoDB, serta pemodelan prediktif menggunakan algoritma regresi linier. Hasil penelitian menunjukkan bahwa variabel seperti jumlah kasus baru, kepadatan penduduk, dan jumlah fasilitas kesehatan memiliki pengaruh signifikan terhadap tingkat kematian. Visualisasi data mendukung temuan ini, di mana wilayah dengan ketersediaan fasilitas yang lebih baik cenderung memiliki angka kematian yang lebih rendah. Penelitian ini menegaskan pentingnya penguatan sistem informasi kesehatan berbasis big data sebagai strategi untuk meningkatkan ketahanan sistem kesehatan nasional dalam menghadapi krisis di masa mendatang.

Eksplorasi Pola Interaksi Pengguna Microblogging dengan Big Data dan Visualisasi Interaktif Hutapea, Oppir; Sitorus, Lamria; Siallagan, Desrico Hizkia; Malau, Mega Paramita; Panjaitan, Frans; Pardede, Inggrid; Sianipar, Roulina
Jurnal Rekayasa Teknologi Informasi (JURTI) Vol 9, No 2 (2025): Jurnal Rekayasa Teknologi Informasi (JURTI)
Publisher : Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/jurti.v9i2.20122

Perkembangan platform microblogging menghasilkan volume data yang sangat besar, menimbulkan tantangan dalam memahami pola interaksi pengguna secara efektif. Penelitian ini penting dilakukan untuk mengidentifikasi dinamika sosial dan tren topik melalui analisis big data dan visualisasi interaktif. Tujuan dari penelitian ini adalah mengembangkan metode eksplorasi pola interaksi pengguna dengan memanfaatkan teknik pengolahan data besar dan visualisasi yang interaktif. Metode yang digunakan meliputi pengumpulan data dari platform microblogging, analisis menggunakan algoritma clustering dan analisis jaringan, serta penyajian data melalui visualisasi dinamis. Hasil penelitian menunjukkan bahwa visualisasi interaktif mampu mempercepat pemahaman pola sosial dan tren informasi dalam data besar, serta memfasilitasi pengambilan keputusan yang lebih akurat. Kesimpulan ini menegaskan pentingnya penerapan big data dan visualisasi dalam memahami dinamika pengguna microblogging, serta memberikan kontribusi signifikan terhadap pengembangan sistem analisis sosial berbasis teknologi tersebut.

Penerapan Convolutional Neural Network Dan DenseNet121 untuk Identifikasi Penyakit Daun Jagung Di Daerah Toba Hutapea, Oppir; Ford Lumban Gaol; Takuro Matsuo
JURIKOM (Jurnal Riset Komputer) Vol. 12 No. 4 (2025): Agustus 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i4.8646

Corn is one of the most important agricultural commodities in the Toba region of North Sumatra. However, its productivity is often reduced due to foliar diseases that appear prior to harvest. The three most commonly observed leaf diseases include leaf spot, blight, and rust. To support early detection efforts among local farmers, this study proposes an image-based classification system employing the Convolutional Neural Network (CNN) algorithm and the DenseNet121 model as a transfer learning approach. The primary objective of this research is to automatically identify the type of disease affecting corn leaves using image data, thereby enabling farmers to promptly implement appropriate countermeasures. A series of experiments were conducted to evaluate various model configurations, including different activation functions (ReLU and Tanh), adjustments to learning rates, and the tuning of other hyperparameters such as optimizers and preprocessing methods (normalization, rotation augmentation, zooming, and contrast adjustments). The results demonstrate that DenseNet121, when trained with an optimal learning rate of 0.001, achieved the highest accuracy of 97%, outperforming the custom-built CNN model which attained an accuracy of 95%. The combination of effective preprocessing techniques and hyperparameter tuning significantly contributed to the improved performance of the models. This study highlights the potential of image-based plant disease detection technologies in agriculture, particularly in aiding real-time decision-making, enhancing land management efficiency, and supporting increased corn yield.

Penerapan Convolutional Neural Network Dan DenseNet121 untuk Identifikasi Penyakit Daun Jagung Di Daerah Toba Hutapea, Oppir; Ford Lumban Gaol; Takuro Matsuo
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 4 (2025): Agustus 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i4.8646

Corn is one of the most important agricultural commodities in the Toba region of North Sumatra. However, its productivity is often reduced due to foliar diseases that appear prior to harvest. The three most commonly observed leaf diseases include leaf spot, blight, and rust. To support early detection efforts among local farmers, this study proposes an image-based classification system employing the Convolutional Neural Network (CNN) algorithm and the DenseNet121 model as a transfer learning approach. The primary objective of this research is to automatically identify the type of disease affecting corn leaves using image data, thereby enabling farmers to promptly implement appropriate countermeasures. A series of experiments were conducted to evaluate various model configurations, including different activation functions (ReLU and Tanh), adjustments to learning rates, and the tuning of other hyperparameters such as optimizers and preprocessing methods (normalization, rotation augmentation, zooming, and contrast adjustments). The results demonstrate that DenseNet121, when trained with an optimal learning rate of 0.001, achieved the highest accuracy of 97%, outperforming the custom-built CNN model which attained an accuracy of 95%. The combination of effective preprocessing techniques and hyperparameter tuning significantly contributed to the improved performance of the models. This study highlights the potential of image-based plant disease detection technologies in agriculture, particularly in aiding real-time decision-making, enhancing land management efficiency, and supporting increased corn yield.

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