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All Journal JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JATI (Jurnal Mahasiswa Teknik Informatika)
Muhathir
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Computer Science & IT Engineering

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ANALISIS PERBANDINGAN MENGGUNKAN METODE TOPSIS DAN WASPAS DALAM PENENTUAN KARYAWAN TELADAN Sri Wahyuni; Rizki Muliono; Nurul Khairina; Muhathir
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 7 No. 1 (2023): JATI Vol. 7 No. 1
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v7i1.6295

Abstract

CV. Multisindo Karya selaku industri konsultan teknologi data pada cara penentuan karyawan teladan saat ini masih menggunakan cara pemantauan langsung. Namun, proses ini dinilai belum efektif dan belum bisa mengakomodir terhadap hal-hal lain yang harusnya mendukung penilaian karyawan sehingga memungkinkan terjadinya kesalahan dalam penilaian dan memperlambat proses penentuan karyawan teladan acuan sedang memakai cara kalkulasi sebagai buku petunjuk, cara ini pastinya menghabiskan durasi cukup lama. Proses penilaian dengan menggunakan beberapa kriteria yang digunakan yaitu , kriteria dalam penilaian karyawan seperti disiplin kerja, tanggung jawab, komunikasi dan kerjasama, pemahaman dan penguasaan pekerjaan, dan inisiatif. Dengan memakai metode Technique for order Performance by Similarity to Ideal Solution, serta metode Weighted Aggregated Sum Product Assesment penulis merancang suatu aplikasi yang dapat melaksanakan cara penentuan karyawan teladan dengan hasil yang dapat dibanding antara kedua tata cara. Bersumber pada hasil kalkulasi dari tata cara TOPSIS memberikan hasil Ira Astriani Saragih dengan angka TOPSIS 0, 748, Serta menggunakan tata cara WASPAS memberikan hasil Ira Astriani Saragih dengan angka WASPAS 0,960. Dan tingkat akurasi yang didapat metode TOPSIS ialah 49,67% dan WASPAS ialah 50,33%.
Mobilenetv2 Analysis in Classification Diseases On Mango Leaves Simangunsong, Roy Candra; Muhathir
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 8 No. 3Spc (2025): Special Issues 2025: Innovations in Predictive Analytics and Sentiment Analy
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v8i3Spc.14430

Abstract

This study aims to analyze the performance of the MobileNetV2 model in classifying diseases on mango leaves, consisting of three classes: capmodium, collectricu, and normal leaves. The dataset used contains 1500 images, with 80% allocated for training data, 10% for testing data, and 10% for validation data. The model was trained using a deep learning approach to identify mango leaf diseases based on the visual patterns present in each class. The results show that the MobileNetV2 model achieved an accuracy of 90%, a precision of 91%, a recall of 90%, and an F1-score of 89%. These findings highlight the potential of MobileNetV2 as an effective tool for automatically detecting mango leaf diseases. Therefore, this study is expected to contribute to the development of technology-based solutions in the agricultural sector, particularly in supporting farmers in identifying diseases quickly and accurately, thereby improving mango crop productivity.
Co-Authors Nurul Khairina Rizki Muliono Simangunsong, Roy Candra Sri Wahyuni