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All Journal JurTI (JURNAL TEKNOLOGI INFORMASI) Jurnal Mantik HOAQ (High Education of Organization Archive Quality) : Jurnal Teknologi Informasi
Erna Rosani Nubatonis
STIKOM Uyelindo Kupang
Computer Science & IT Economics, Econometrics & Finance Languange, Linguistic, Communication & Media

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System Monitoring Tingkat Kekeruhan Air dan Pemberian Pakan Ikan Pada Aquarium Berbasis IOT Yohanes Karmani; Yohanes Suban Belutowe; Erna Rosani Nubatonis
(JurTI) Jurnal Teknologi Informasi Vol 6, No 1 (2022): JUNI 2022
Publisher : Universitas Asahan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36294/jurti.v6i1.2598

Abstract

Internet of Things (IoT) adalah sebuah konsep dimana suatu objek yang memiliki kemampuan untuk mentransfer data melalui jaringan Internet tanpa memerlukan interaksi manusia ke manusia atau manusia ke komputer. Sistem Monitoring Tingkat Kekeruhan Air dan Pemberian Pakan Ikan pada aquarium berbasis Iot (Internet of Things) dalam pemberian pakan ikan berupa pelet, dan kejernihan air dalam aquarium karena ikan membutuhkan air yang jernih. pekerjaan yang rutin dilakukan pada aquarium adalah memberi pakan ikan dan mengganti air yang sudah keruh agar terlihat bersih dan menciptakan kondisi yang baik untuk ikan tersebut. Komponen yang digunakan meliputi ESP8266 nodeMCU, Sensor turbidity, Sensor suhu, Servo, Pompa air mini, dan Aplikasi sebagai Interface Untuk mengetahui tingkat kekeruhan air pada aquarium.
Implementation Of GLCM (Gray Level Co-Occurrence Matrix) & KNN( K-Nearest Neighbor ) For Classification Of Fiber Root Plant Types Based On Leaf Image Nubatonis, Erna Rosani
Jurnal Mantik Vol. 8 No. 2 (2024): August: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.v8i2.5397

Abstract

This study aims to implement the GLCM (Gray Level Co-Occurrence Matrix) and KNN (K-Nearest Neighbor) methods in the classification of fiber root species based on leaf images. Fibrous roots are the most common root type in certain plants, and classifying plant species based on leaf image can provide useful information in contacting plants. The GLCM method is used to extract texture features from leaf images. The GLCM matrix describes the relative occurrence of pixel pairs with different gray intensities in the image. These features can provide information about leaf texture that can be used in classification. Furthermore, the KNN algorithm is used to classify plant types based on the extracted features. The dataset used in this study consists of a number of leaf images representing several different types of fiber root plants. Image processing includes pre-processing to obtain a clean image and ensure consistency of image size. After feature extraction using the GLCM method, these features are used as input for the KNN algorithm. KNN is used to classify unknown leaf images into one of the plant classes that have been previously trained. The experimental results show that the GLCM and KNN methods can provide good results in the classification of fiber root plant species based on leaf images. High classification accuracy indicates the effectiveness of this method in identifying plant species based on textural features of leaf images. Thus, this method can be a useful tool in the field of plant recognition and other applications that involve identifying plant species based on leaf images
ANALISA DAN PERANCANGAN PREDIKSI TINGKAT PRESENTASI MAHASISWA BARU MASUK SEBAGAI MAHASISWA AKTIF DI STIKOM UYELINDO KUPANG MENGGUNAKAN ROUGHT SET Nubatonis, Erna Rosani
HOAQ (High Education of Organization Archive Quality) : Jurnal Teknologi Informasi Vol. 10 No. 1 (2019): Jurnal HOAQ - Teknologi Informasi
Publisher : STIKOM Uyelindo Kupang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52972/hoaq.vol10no1.p23-29

Abstract

Acceptance of new students is the most important part of STIKOM UYELINDO Kupang as one of the benchmarks for the progress of the campus in the future. In the process of admitting new students (PMB), prospective new students must go through several stages of registration until the stage of filling out the KRS, so that the students concerned are legitimately declared as active students of STIKOM UYELINDO KUPANG. However, many cases occur that not all students arrive at the final stage of filling in the KRS to be declared as active students. Problems that occur result in the division responsible for new students difficult to predict that prospective students concerned in the process of admitting new students, will go through the process until the status of filling KRS or not, and also affect the prediction of the number of new student achievement. This study aims to find out and recognize the pattern of classification of new student registration status so that the level of presentation of new students entering the STIKOM UYELINDO KUPANG can be made by applying the rough set algorithm. In the process of applying Rough Set, it will produce a rule as a rule or pattern for classification of new student registration status data. The data used in this study is the data of new student registration in 2016-2018 with a total record of 579 records. The results of this study are expected to be an important input for the responsibility of new students and high school education institutions, in the strategy of screening new students to achieve the target of better new student admissions.
Co-Authors Yohanes Karmani Yohanes Suban Belutowe