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All Journal Idealis : Indonesia Journal Information System Jurnal Ketenagakerjaan
Arif Hermawan, Arif
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Law, Crime, Criminology & Criminal Justice Social Sciences

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Kesenjangan Kondisi Pengangguran Lulusan SMK/MAK di Indonesia: Analisis Antargender dan Variabel-Variabel yang Memengaruhinya Hermawan, Arif; Mufiedah, Maziyyatul; Madina, Virginia; Santika, Zukhrufiyah Mei; Kasim, Muhammad Faturahman; Siagian, Tiodora Hadumaon
Jurnal Ketenagakerjaan Vol 18 No 3 (2023)
Publisher : Pusat Pengembangan Kebijakan Ketenagakerjaan Kementerian Ketenagakerjaan Republik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47198/jnaker.v18i3.246

Abstract

Indonesia masih memerlukan usaha yang cukup besar untuk mengatasi pengangguran, terutama bagi lulusan SMK/MAK. Menurut Badan Pusat Statistik (BPS), lulusan Sekolah Menengah Kejuruan (SMK)/Madrasah Aliyah Kejuruan (MAK) merupakan kontributor terbesar Tingkat Pengangguran Terbuka (TPT) Indonesia pada Agustus 2021. Selain itu, TPT lulusan SMK/MAK antara perempuan dan laki-laki masih memiliki perbedaan yang cukup besar. Berdasarkan Survei Angkatan Kerja Nasional (Sakernas) Agustus 2021, TPT perempuan lulusan SMK/MAK mencapai 10,17 persen sementara TPT laki-laki lulusan SMK/MAK mencapai 11,57 persen. Hal ini menunjukkan bahwa masih terdapat ketimpangan gender dalam aspek ketenegakerjaan pada lulusan SMK/MAK di Indonesia. Kesetaraan gender dalam ketenagakerjaan perlu diciptakan karena memiliki dampak pada pertumbuhan ekonomi. Oleh karena itu, penelitian ini bertujuan untuk mengetahui variabel yang signifikan memengaruhi status menganggur lulusan SMK/MAK antargender di Indonesia pada tahun 2021. Penelitian ini menggunakan data Sakernas Agustus 2021 yang dianalisis dengan metode regresi logistik biner. Hasil penelitian ini menunjukkan bahwa variabel yang berpengaruh secara signifikan terhadap status menganggur pada lulusan SMK/MAK perempuan adalah umur, klasifikasi tempat tinggal, status perkawinan, periode lulus, status disabilitas, dan klasifikasi jurusan sementara variabel yang berpengaruh signifikan terhadap status menganggur pada lulusan SMK/MAK laki-laki adalah umur, status perkawinan, periode lulus, status disabilitas, dan klasifikasi jurusan.
TOMATO CLASSIFICATION BASED ON VARIETY WITH RGB FEATURE EXTRACTION AND NAÏVE BAYES ALGORITHM Widyastuti, Evi; Hermawan, Arif; Avianto, Donny
IDEALIS : InDonEsiA journaL Information System Vol. 8 No. 1 (2025): Jurnal IDEALIS Januari 2025
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/idealis.v8i1.3370

Abstract

Tomato is a fruit-category vegetable plant that is easy to cultivate in various locations. The diversity of tomato varieties, such as Red Zebra Tomato, Green Zebra Tomato, and Kumato, often makes rapid and accurate variety identification challenging. Misclassification can impact the selection of environmental conditions and pest or disease management, ultimately leading to suboptimal cultivation results. Currently, research primarily focuses on tomato shape, diseases, and ripeness levels, while cultivar classification based on color characteristics remains limited. This study aims to develop a method for classifying tomato cultivars based on RGB color features using the Naïve Bayes algorithm. The research was conducted by collecting 45 tomato images with similar shapes but different colors (red, green, and dark red). The research stages include RGB feature extraction, data rounding, splitting training and test data with a 70:30 ratio, and classification using Naïve Bayes. A re-evaluation was performed by removing specific color attributes to assess their impact on accuracy. This study is expected to support rapid and accurate tomato variety identification, enhance efficiency in modern agriculture, and expand the application of technology in the agricultural industry to achieve advanced, self-sufficient, and modern farming. The results show that the RGB feature extraction method and the Naïve Bayes algorithm can classify tomato cultivars with an accuracy of up to 78.57%. The RG color attributes have the most significant impact on accuracy, reaching 85.71%.
Co-Authors Donny Avianto Kasim, Muhammad Faturahman Madina, Virginia Mufiedah, Maziyyatul Santika, Zukhrufiyah Mei Siagian, Tiodora Hadumaon Widyastuti, Evi