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All Journal JOURNAL OF SCIENCE AND SOCIAL RESEARCH JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) Worksheet : Jurnal Akuntansi Jurnal Pengabdian Masyarakat dan Riset Pendidikan Jurnal Sains dan Teknologi Informasi Horizon: Indonesian Journal of Multidisciplinary
Tarigan, Devanta Abraham
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Public Health Social Sciences Other

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KINERJA KEUANGAN PERBANKAN DALAM PERSPEKTIF CAMEL: EVALUASI EMPIRIS TERHADAP PT BANK NATIONAL NOBU TBK Anshor, Khairi; Soraya, Nurhaflah; Ginting, Rika Githamala; Arifyanto, Gatot Teguh; Tarigan, Devanta Abraham
Worksheet : Jurnal Akuntansi Vol 4, No 2 (2025)
Publisher : UNIVERSITAS DHARMAWANGSA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/wjs.v4i2.6413

Abstract

Penelitian ini bertujuan untuk menganalisis tingkat kesehatan keuangan PT. Bank Nationalnobu Tbk selama periode 2020 hingga 2023 berdasarkan pendekatan rasio keuangan yang mencakup Capital Adequacy Ratio (CAR), Non-Performing Loan (NPL), Return on Assets (ROA), Net Profit Margin (NPM), dan Loan to Deposit Ratio (LDR). Metode analisis yang digunakan bersifat deskriptif kuantitatif dengan mengacu pada standar penilaian kesehatan bank yang ditetapkan oleh Bank Indonesia. Hasil penelitian menunjukkan bahwa bank berada dalam kategori cukup sehat pada tahun 2020 hingga 2022, ditandai oleh rasio CAR dan NPL yang stabil dan tinggi. Namun, pada tahun 2023, terjadi penurunan peringkat kesehatan bank menjadi kurang sehat yang disebabkan oleh peningkatan signifikan pada rasio LDR hingga 99,67%, yang mencerminkan potensi risiko likuiditas. Dengan demikian, meskipun kinerja permodalan dan kualitas aset cukup baik, bank perlu meningkatkan efisiensi operasional dan pengelolaan likuiditas untuk menjaga kestabilan keuangannya secara berkelanjutan.
Analisis Solar Tracking pada Pembangkit Listrik Tenaga Surya Sebagai Sumber Tenaga Lampu Jalan: Penelitian Devanta Abraham Tarigan; Alexander Sebayang; Efrata Tarigan; Liwat Tarigan
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 3 No. 4 (2025): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 3 Nomor 4 (April 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v3i4.2079

Abstract

The utilization of solar energy in Indonesia is still very low despite its huge potential. One of the causes of the low efficiency of solar panels is static installation that does not follow the direction of sunlight. To overcome this, an automatic solar tracker system was designed and built on a 20WP Solar Power Plant (PLTS) as a source of energy for street lights. This research uses a design and experiment method, which involves the development of an ESP32 microcontroller-based system, equipped with a GY-271 compass sensor, INA219 current and voltage sensors, and a DS3231 RTC module. A servo motor is used to automatically adjust the panel's position to follow the direction of the sun. This system is also equipped with an LM2596 Step-Down regulator and is programmed using the Arduino IDE, and can send real-time monitoring data via Wi-Fi to a smartphone. The experimental results show that the solar tracker system is able to increase electrical power up to more than a static panel at the same test time. The panel follows the movement of the sun from east to west, resulting in higher power efficiency throughout the day. Comparison of data between the tracker system and the panel still shows a significant difference in power at each hour of measurement. Testing has begun and has yielded an average power increase of 20.41% compared to static solar panels.
Review Produk Iphone dengan Analasis Sentimen menggunakan Algoritma Text Mining TF-IDF Tarigan, Devanta Abraham
Jurnal Sains dan Teknologi Informasi Vol 4 No 2 (2025): Maret 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jussi.v4i2.7799

Abstract

The iPhone is a product that has become a major concern in society and has become one of the main needs in everyday life. However, sometimes the iPhone often faces several problems that need attention. One problem that is often the main focus is the fairly high price. Therefore, we need a system that can determine the public's view of the iPhone product. This research uses text mining and TF-IDF to determine people's views on iPhone products. Text mining can be defined as the discovery of new, previously unknown information and the automatic extraction of valuable information from text from different sources. Meanwhile, TF-IDF is used to determine the frequency value of words in a document. In this research, sentiment refers to people's views on iPhone products, whether positive or negative. The final result of this sentiment analysis is that the positive sentiment value is 68.65% while the negative sentiment value is 31.35%. This is expected to provide information about the extent to which iPhone products are accepted by the public. By understanding people's sentiments, Apple company can take necessary actions to improve product quality and user satisfaction. Apart from that, this research also introduces the concept of Text Mining and the TF-IDF algorithm as a powerful tool for analyzing text data in the context of sentiment analysis.
PKM Optimalisasi Pendapatan melalui Transformasi Digital BMA: Branding, Marketing dan Accounting untuk Keberlanjutan UMKM Deli Serdang Rahmadani Rahmadani; Nurhaflah Soraya; Muhammad Asrin Jazuli; Khairi Anshor; Devanta Abraham Tarigan
JURPIKAT Vol 6 No 4 (2025)
Publisher : Politeknik Piksi Ganesha Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37339/jurpikat.v6i4.2730

Abstract

Program pengabdian masyarakat ini bertujuan untuk menyelesaikan masalah mitra terkait kemampuan mitra dalam membranding produk bisnis, memasarkan produk secara digital, dan melakukan perhitungan harga pokok produksi. Selain itu kegiatan PKM ini juga memberikan produk ke UMKM berupa program sederhana berbasis MS. Excel untuk menyelesaikan permasalahan akuntansi dan alat perekat agar kemasan lebih menarik. PKM ini penting bagi mitra untuk meningkatkan pendapatan usaha melalui branding, digital marketing. Luaran yang ditargetkan adalah peningkatkan kemampuan mitra sehingga mitra menjadi lebih mandiri dan mampu meningkatkan branding, marketing, dan accounting yang berbasis digital dan terstandar. Sehingga keputusan bisnis yang diambil berdasarkan data akuntansi yang akurat. Dan pada akhirnya meningkatkan penjualan usaha dibarengin dengan peningkatan profit dan kesejahteraan pelaku UMKM guna untuk keberlanjutan ekonomi. Target yang dicapai adalah mitra mampu meningkatkan citra baik produk melalu branding, menggunakan dan memaksimalkan pemasaran dengan berbasis digital marketing berupa sosial media, dan mitra mampu menyusun akuntansi biaya produksi menggunakan excel.
SIMULASI ADAPTIVE PURSUIT ROUTE ALGORITHM (APRA) UNTUK ALGORITMA INTERSEPSI ADAPTIF BERBASIS PREDIKSI DINAMIS UNTUK PERENCANAAN JALUR TARGET BERGERAK DENGAN MANUVER TINGGI Devanta Abraham Tarigan
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 9 No. 3 (2026): June 2026
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i3.6374

Abstract

Intercepting a highly maneuvering moving target remains a difficult planning problem because the target may change heading, accelerate, halt, or reverse without warning, while the pursuer only receives noisy position observations. This study proposes the Adaptive Pursuit Route Algorithm (APRA), an interception path-planning method that combines short-horizon target-state estimation, time-to-go aim-point projection, dynamically weighted cost terms, and a bounded residual that is learned online from past prediction errors. APRA is evaluated against three classical pursuit laws-Pure Pursuit, Proportional Navigation (PN), and Augmented PN (APN)-on a physics-based synthetic dataset of 3,200 trajectories spanning eight motion regimes, yielding 12,800 engagements. APRA attains the best result on every metric: an interception rate of 90.91% (95% CI 0.899-0.919), the shortest mean interception time (9.65 s), the lowest mean prediction error (8.73 units), the highest path efficiency (0.798), and the lowest control energy (48,022). One-way ANOVA confirms that the differences in time, energy, and prediction error are statistically significant (p < 0.001), and a Welch t-test shows APRA is significantly faster than APN (t = -14.48, p < 0.001). The results indicate that disciplined filtering combined with bounded online adaptation is more robust against deceptive and abrupt maneuvers than raw lead-based guidance.
Evaluasi Algoritma Random Forest dan KNN dalam Memprediksi Risiko Diabetes Berdasarkan Fitur Klinis Tarigan, Siti Jamilah Br; Alyiza Dwi Ningtyas; Arif Hamied Nababan; Devanta Abraham Tarigan; Dini Rizqi Dwikunti Siregar
HORIZON: Indonesian Journal of Multidisciplinary Vol. 4 No. 3 (2026): HORIZON: Indonesian Journal of Multidisciplinary
Publisher : Lembaga Intelektual Muda (LIM) Maluku

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54373/hijm.v4i4.6400

Abstract

This study aims to demonstrate the performance of the Random Forest and K-Nearest Neighbors (KNN) algorithms in predicting diabetes risk based on numerical clinical data. The study used a dataset of 757 samples with eight clinical features, namely the number of pregnancies, glucose levels, blood pressure, skin thickness, insulin, body mass index (BMI), familial diabetes predisposition function, and age. The data was divided into 80% training data and 20% testing data, with data scale adjustments to support the classification process. The evaluation results showed that Random Forest produced better performance with an accuracy of 73.7% and an F1-Score of 0.623, compared to KNN with an accuracy of 72.4% and an F1-Score of 0.604. Comparison of classification results showed that Random Forest was able to provide more consistent predictions in distinguishing groups at risk of diabetes from healthy groups. The contribution of this study is to provide an empirical evaluation of the description of two classification algorithms commonly used on numerical clinical data and show that Random Forest is more suitable for the development of a decision support system for diabetes risk prediction. This research can be the basis for the development of more accurate prediction models through the use of broader datasets and other machine learning methods.
Co-Authors Alexander Sebayang Alyiza Dwi Ningtyas Arif Hamied Nababan Arifyanto, Gatot Teguh Dini Rizqi Dwikunti Siregar Efrata Tarigan Ginting, Rika Githamala Khairi Anshor Liwat Tarigan Muhammad Asrin Jazuli Nurhaflah Soraya Rahmadani Rahmadani Tarigan, Siti Jamilah Br