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All Journal Jurnal Simantec Network Engineering Research Operation [NERO] KERNEL: Jurnal Riset Inovasi Bidang Informatika dan Pendidikan Informatika Teknologika Jurnal Geosaintek
Hendro Nugroho
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Industrial & Manufacturing Engineering Mechanical Engineering Other

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OBJECT TRACKING PADA SEBUAH VIDEO DENGAN MENGGUNAKAN METODE HARRIS CORNER DETECTION DAN OPTICAL FLOW Hendro Nugroho; Siti Agustini
Network Engineering Research Operation Vol 5, No 2 (2020): NERO
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Bagian penelitian pada komputer vision salah satunya adalah objek tracking, dimana objek yang diinginkan akan diikuti dengan adanya tanda pada objek tersebut. Pada penelitian objek tracking ini menggunakan video dengan durasi 02:03 menit, size 21,5 MB, panjang frame 720x1280 piksel, jumlah frame 3029 dan jenis file video ASF. Untuk pengujian objek tracking berdasarkan pada video berdasarkan tiga jenis posisi objek dengan kamera yaitu (1) objek bergerak kamera posisi tetap, (2) objek tetap posisi kamera bergerak, dan (3) objek bergerak kamera bergerak. Metode dalam penelitian objek tracking menggunakan optical flow dan Harris corner detection. Langkah-langkah untuk mendapatkan hasil objek tracking adalah praproses yang menggunakan metode grayscale, metode gerak optical flow dan metode deteksi tracking Harris corner detection. Hasil dari objek tracking menggunakan metode tersebut adalah terdapat titik warna merah pada objek yang terdapat pada video. Dalam pengujian pada video terdapat kendala pada buffering proses objek tracking yang disebabkan oleh komputasi komputer yang tidak bagus.
KLASIFIKASI DAUN HERBAL MENGGUNAKAN METODE NAÏVE BAYES CLASSIFIER DAN KNEAREST NEIGHBOR Febri Liantoni; Hendro Nugroho
Jurnal Simantec Vol 5, No 1 (2015)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/simantec.v5i1.1009

Abstract

ABSTRAKPerkembangan ilmu tanaman telah mengalami kemajuan yang pesat, khususnya ilmu mengenai tanaman herbal. Tanaman herbal memiliki banyak manfaat bagi kehidupan manusia yaitu sebagai penyedian oksigen, bahan makanan, obat-obatan, maupun bahan kosmetika. Untuk mengetahui jenis-jenis tanaman herbal dapat dilakukan dengan proses klasifikasi. Klasifikasi tanaman herbal dapat dilakukan dengan cara mengidentifikasi bentuk citra daun dari tanaman tersebut. Proses klasifikasi tanaman herbal memerlukan ekstraksi fitur dari bentuk dari tanaman tersebut. Pada penelitian ini, fitur invariant moment dan fitur geometri digunakan untuk ekstraksi fitur daun herbal. Fitur yang digunakan berjumlah 10 fitur. Ada beberapa macam metode klasifikasi yang biasa digunakan. Pada penelitian ini metode klasifikasi yang digunakan adalah metode Naïve Bayes Classifier dan K-Nearest Neighbor (KNN). Metode Naïve Bayes Classifier merupakan metode Bayesian Learning yang paling cepat dan sederhana. Sedangkan metode KNN dapat melakukan klasifikasi dengan cepat berdasarkan jarak terdekat diantara objek data. Berdasarkan hasil uji coba yang dilakukan, penggunaan metode Naïve Bayes Classifier didapatkan nilai akurasi sebesar 75%, sedangkan dengan menggunakan metode K-Nearest Neighbor didapatkan nilai akurasi sebesar 70,83%. Hal ini menunjukkan bahwa kinerja metode Naïve Bayes Classifier lebih baik dibandingkan metode KNN.Kata Kunci: Fitur Invariant Moment, Fitur Geometri, Naïve Bayes Classifier, K-Nearest Neighbor, Bayesian Learning. ABSTRACTScience of the plant has made progress, particularly knowledge about herbs. Herb has many benefits for human life as provision of oxygen, foodstuffs, pharmaceuticals, and cosmetics. To determine the types of herbs with the classification process. Classification of herbs conducted by identifying the shape of the image of the leaves of these plants. Herbal plant classification process requires the extraction of features from the shape of plant. In this study, moment invariant features and geometrical feature is used for feature extraction of herbal leaves.Features used amounted to 10 features. There are several kinds of commonly used classification method. In this study, the classification method used is the method Naïve Bayes classifier and K-Nearest Neighbor (KNN). Naïve Bayes classifier is Bayesian Learning method of the most rapid and simple. While the KNN method can perform fast classification is based on the shortest distance between data objects. Based on the results of tests conducted, the use of methods Naïve Bayes Classifier accuracy values obtained by 75%, while using K-Nearest Neighbor value obtained an accuracy of 70.83%.These results indicate that the performance of Naïve Bayes Classifier method is better than KNN method.Keywords: Invariant Moment Feature, Geometrical Feature, Naïve Bayes Classifier, K-Nearest Neighbor, Bayesian Learning
ANALISA PENGARUH VARIASI KUAT ARUS TERHADAP KEKUATAN TARIK SAMBUNGAN LAS SMAW DENGAN MATERIAL BAJA KARBON RENDAH DENGAN PROFIL BESI SIKU MENGGUNAKAN ELEKTRODA E6013 MUHAMAD GILANG KRISWANDI; JATIRA; HENDRO NUGROHO
Jurnal Teknologika Vol 12 No 1 (2022): Jurnal Teknologika
Publisher : Sekolah Tinggi Teknologi Wastukancana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (397.406 KB) | DOI: 10.51132/teknologika.v12i1.155

Abstract

Effect of welding current using SMAW welding method with E6013 electrode on the tensile strength of angle bar. Where this study aims to determine how much influence the welding current SMAW welding with electrodes E6013 diameter 2 mm on the tensile strength of the welding results. In this study, the materials used was angled iron, them V seam was made, which was welded with a current of 90 Ampere, 100 Ampere and 115 Ampere, with an E6013 electrode with a diameter 2 mm, thern tensile testing was carried out. The highest tensile strength results at 90 Ampere welding current, namely 97238 N, while the lowest occurred at 110 Ampere current, namely 1060.56 N. Current variations greatly affect the tensile strength.
Sistem Informasi Logistik PT Fajar Multiguna MUHAMMAD EKO PUJIANTO; FAJAR NUGRAHA; BAGUS RIZKITA; Hendro Nugroho
KERNEL: Jurnal Riset Inovasi Bidang Informatika dan Pendidikan Informatika Vol 3, No 1 (2022)
Publisher : Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.kernel.2022.v3i1.2485

Abstract

PT Fajar Multiguna is a start-up company engaged in the Procurement of Government Goods/Services and Logistics for the delivery of procurement packages. Quite a lot of other companies that use this delivery service. The system that runs is still manual, there is no website to track the whereabouts of packages that are in transit. An information system is needed to make it easier for customers to track shipments. For this reason, a web-based Logistics Information system was designed with the Laravel PHP framework programming language. This information system will make it easier for PT Fajar Multiguna’s customers to track the delivery of their packages, on the other hand PT Fajar Multiguna will also find it easier to handle the delivery data.
SEISMIC SITE QUALITY ASSESSMENT IN NORTH SUMATRA USING SPECTRAL DENSITY ANALYSIS AND MACHINE LEARNING-BASED CLUSTERING Triya Fachriyeni; Katherin Indriawati; Kevin W. Pakpahan; Irfan Rifani; Anne M. M. Sirait; Yusran Asnawi; Hendro Nugroho; Andrean V. H. Simanjuntak
Jurnal Geosaintek Vol. 11 No. 3 (2025)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j25023659.v11i3.8972

Abstract

Seismic noise strongly influences the accuracy and reliability of earthquake monitoring, particularly in tectonically active regions such as North Sumatra. This study investigates the quality of seismic stations by analyzing noise characteristics using Power Spectral Density (PSD), Probability Density Functions (PDFs), and machine learning clustering. PSD was computed through the Fast Fourier Transform (FFT) and compared against the New High Noise Model (NHNM) and New Low Noise Model (NLNM) benchmarks. Noise variability was further quantified using PDFs, while fuzzy c-means (FCM) clustering was applied to classify temporal noise patterns. Results from the MUTSI seismic station demonstrate strong diurnal and weekly variability, with horizontal components (SHE and SHN) exhibiting significantly higher noise levels and fluctuations than the vertical component (SHZ). Noise amplitudes peaked during morning hours (06:00–09:00 UTC), correlating with anthropogenic activity, and decreased substantially at night, indicating that optimal recording conditions occur during late evening to early morning. FCM clustering identified five dominant noise regimes, separating stable low-noise baselines from sporadic high-noise anomalies likely associated with human activity or instrumental disturbances. These findings highlight the importance of integrating spectral analysis with clustering techniques to evaluate seismic station performance, improve real-time monitoring, and guide optimal site selection and operational scheduling for earthquake detection.
IDENTIFIKASI MISORIENTATION SENSOR SEISMOGRAF BMKG MENGGUNAKAN METODE P-WAVE PARTICLE MOTION: STUDI DI PULAU SUMATERA BAGIAN UTARA Chichi Nurhafizah; Purwadi Agus Darwito; Wijayanto Wijayanto; Hendro Nugroho
Jurnal Geosaintek Vol. 11 No. 3 (2025)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j25023659.v11i3.8974

Abstract

Analisis polaritas gelombang P dapat digunakan untuk mengevaluasi keakuratan orientasi sensor seismograf. Ketidaktepatan orientasi (misorientation) berpotensi menurunkan akurasi penentuan mekanisme sumber gempa bumi. Penelitian ini menganalisis orientasi sensor seismograf BMKG di wilayah Sumatera bagian utara dengan menggunakan metode P-Wave Particle Motion. Sebanyak 58 sensor berhasil dianalisis secara kuantitatif dan menghasilkan estimasi sudut orientasi aktual. Hasil menunjukkan bahwa sensor memiliki variasi misorientation berkisar antara 1° hingga 46°, dengan 15 sensor di antaranya menunjukkan nilai misorientation signifikan, yaitu RSSM (46°), MASM (28°), PAASI (27°), SLSM (24°), TPTI (23°), GESM dan SKSI (22°), TKSM dan BESM (20°), SMSM (19°), LTSM dan TASI (18°), PDSI (17°), serta PLSI dan KASAI (16°). Temuan ini menunjukkan ketidaksesuaian arah pemasangan sensor terhadap arah utara sejati, yang dapat menurunkan kualitas interpretasi data seismik, sehingga evaluas misorientation sensor berbasis data perlu dilakukan secara berkala guna menjaga reliabilitas data seismik nasional. Penelitian ini merupakan evaluasi sistematis berskala besar pertama terhadap orientasi sensor BMKG di Indonesia dengan memanfaatkan analisis gerak partikel gelombang teleseismik, sehingga memberikan dasar ilmiah penting bagi pengembangan standar kualitas jaringan seismograf di masa mendatang.
SEISMOTECTONIC STUDY OF THE SIBOLANGIT, NORTH SUMATRA REGION BASED ON DOUBLE-DIFFERENCE RELOCATION Nesia S. Marbun; Aulia A. Aisjah; Anne M. M. Sirait; Yusran Asnawi; Hendro Nugroho; Andrean V. H. Simanjuntak
Jurnal Geosaintek Vol. 11 No. 3 (2025)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j25023659.v11i3.8986

Abstract

Sumatra is one of the most seismically active regions in the world due to the oblique convergence between the Indo-Australian and Eurasian plates, where strain is partitioned between the Sunda megathrust and the Great Sumatran Fault (GSF). While most seismicity in North Sumatra occurs along mapped strands of the GSF, several damaging earthquakes have occurred outside known fault zones, raising critical questions about hidden seismogenic structures. This study investigates the seismotectonic framework of the Karo region, with a focus on the 2017 Karo earthquake (Mw 5.6), using the double-difference relocation method. A dataset of local earthquakes recorded by the Indonesian Agency for Meteorology, Climatology, and Geophysics (BMKG) was analyzed to refine hypocenter locations, reduce uncertainties, and identify seismic clusters. Relocation results significantly improved spatial resolution, reducing average location errors to less than 3 km, and revealed clustered seismicity along a northwest–southeast trending structure offset from the Renun Fault. Depth cross-sections indicate brittle faulting within the upper crust (5–12 km), and the aftershock alignment suggests the presence of an unmapped subsidiary fault accommodating dextral shear. Comparisons with similar studies across Sumatra and Java confirm that off-fault seismicity is a common but often overlooked contributor to regional hazard. These findings underscore the importance of integrating relocated seismicity into national hazard models to account for hidden faults. By providing improved fault geometry and seismotectonic insights, this study enhances the understanding of earthquake sources in North Sumatra and supports future efforts in seismic hazard mitigation and disaster risk reduction in one of Indonesia’s most vulnerable regions.
Co-Authors Andrean V. H. Simanjuntak Anne M. M. Sirait Aulia A. Aisjah BAGUS RIZKITA Chichi Nurhafizah Fajar Nugraha Febri Liantoni irfan rifani Jatira Katherin Indriawati Kevin W. Pakpahan MUHAMAD GILANG KRISWANDI MUHAMMAD EKO PUJIANTO Nesia S. Marbun Purwadi Agus Darwito Siti Agustini Triya Fachriyeni Wijayanto Wijayanto Yusran Asnawi