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All Journal JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Ilmiah Bongaya (JIB)
Andi Tenriola
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Computer Science & IT Economics, Econometrics & Finance Social Sciences

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Pengaruh Reward Dan Punishment Terhadap Motivasi Kerja Karyawan Andi Tenriola; Arfah, Faizal
Jurnal Ilmiah Bongaya Vol. 7 No. 2 (2023): Desember 2023 : Jurnal Ilmiah Bongaya
Publisher : LPPM STIEM

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Abstract

Penelitian ini bertujuan untuk mengetahui pengaruh reward dan punishmenrt terhadap motivasi kerja karyawan PT. Anugerah Vata Abadi Cabang Bollangi Kabupaten Gowa. Responden penelitian ini adalah 32 orang karyawan tetap di PT. Anugerah Vata Abadi. Penelitian dilaksanakan pada bulan Juli 2022 - Agustus 2022. Penelitian ini menggunakan pendekatan kuantitatif. Teknik analisis data yang digunakan yaitu analisis regresi linear berganda. Hasil penelitian ini menunjukkan bahwa variabel reward secara parsial berpengaruh positif dan signifikan terhadap motivasi kerja, sedangkan variabel punishment secara parsial berpengaruh positif tapi tidak signifikan terhadap motivasi kerja. Secara simultan variabel reward dan punishment berpengaruh secara positif dan signifikan terhadap motivasi kerja. Keterbatasan penelitian ini adalah lokasi penelitian yang jauh, data yang diperoleh pada saat pembagian kuesioner tidak banyak karena keterbatasan populasi di PT. Anugerah Vata Abadi.
Peran Job Demands dan Job Resources dalam Meningkatkan Work Engagement di Tempat Kerja Abdul Mansyur; Alimuddin Syar; Amelia; Andi Tenriola
Jurnal Ilmiah Bongaya Vol. 8 No. 2 (2024): 2024 : Jurnal Ilmiah Bongaya
Publisher : LPPM STIEM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37888/jib.v8i2.686

Abstract

Penelitian ini bertujuan untuk mengetahui dan menganalisis pengelolaan keuangan daerah, terhadap akuntabilitas kinerja keuangan, dan transparansi pemerintah daerah Kabupaten Sinjai, penelitian kualitatif. Dalam penelitian ini menggunakan metode deskriptif dengan populasi sebagai alat statistik. Penelitian ini adalah seluruh organisasi perangkat daerah Kabupaten Sinjai yang berjumlah 40 instansi perangkat daerah. Sampel dalam penelitian perangkat daerah ini sebanyak 66 orang, yaitu dinas-dinas dinas Sinjai. Hasil penelitian data kecamatan berjumlah 22 diuji dengan uji asumsi klasik (Uji Validitas dan Reabilitas), Uji Normalitas, Analisis data menggunakan Uji Multikolonieritas. Sedangkan teknik pengumpulan data yang digunakan adalah regresi linier berganda. Setelah itu dianalisis dengan menggunakan program SPSS (Statistical Product and Service Solution) Versi 26. Berdasarkan hasil uji hipotesis menunjukkan bahwa Pengelolaan Keuangan Daerah tidak berpengaruh dan tidak signifikan terhadap Keuangan Pemerintah Daerah, sedangkan Akuntabilitas Kinerja Daerah berpengaruh positif dan Kinerja Keuangan Daerah berpengaruh signifikan terhadap Keuangan Pemerintah Daerah, dan Transparansi berpengaruh positif dan signifikan terhadap Kinerja Keuangan Pemerintah Daerah.
CLASSIFICATION OF PAPAYA NUTRITION BASED ON MATURITY WITH DIGITAL IMAGE AND ARTIFICIAL NEURAL NETWORK Andi Ahmad Taufiq; Hanum Zalsabilah Idham; Muh Fuad Zahran Firman; Andi Baso Kaswar; Dyah Darma Andayani; Muhammad Fajar B; Abdul Muis Mappalotteng; Andi Tenriola
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.7070

Abstract

Papaya is a tropical fruit with high nutritional content and significant health benefits. Nutritional components such as sugars, vitamin C, and fibre are strongly influenced by ripeness level. Identifying these nutrients usually requires laboratory tests that are time-consuming and rely on sophisticated equipment. Previous studies have focused on classifying ripeness levels, yet none have specifically addressed the classification of nutritional content. This study proposes a classification system for papaya nutrition based on ripeness using digital image processing and artificial neural networks (ANN). The method consists of six stages: image acquisition, preprocessing, segmentation, morphology, feature extraction, and classification with a trained ANN model. Experiments were conducted to evaluate feature combinations, including colour and texture features. The combination of LAB colour features and texture features-contrast, correlation, energy, and homogeneity-produced the best results. Testing on 75 images achieved an average precision of 97.22%, recall of 96.67%, F1-Score of 96.80%, and accuracy of 97.33%, with an average computation time of 0.02 seconds per image. These findings indicate that the proposed method provides fast and highly accurate classification of papaya’s nutritional content, offering a practical alternative to laboratory testing. Nevertheless, the study is limited by the relatively small dataset and controlled acquisition environment. Future research should extend the dataset, incorporate deep learning approaches, and validate performance under real-world conditions to enhance robustness and generalization
CLASSIFICATION OF PAPAYA NUTRITION BASED ON MATURITY WITH DIGITAL IMAGE AND ARTIFICIAL NEURAL NETWORK Andi Ahmad Taufiq; Hanum Zalsabilah Idham; Muh Fuad Zahran Firman; Andi Baso Kaswar; Dyah Darma Andayani; Muhammad Fajar B; Abdul Muis Mappalotteng; Andi Tenriola
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.7070

Abstract

Papaya is a tropical fruit with high nutritional content and significant health benefits. Nutritional components such as sugars, vitamin C, and fibre are strongly influenced by ripeness level. Identifying these nutrients usually requires laboratory tests that are time-consuming and rely on sophisticated equipment. Previous studies have focused on classifying ripeness levels, yet none have specifically addressed the classification of nutritional content. This study proposes a classification system for papaya nutrition based on ripeness using digital image processing and artificial neural networks (ANN). The method consists of six stages: image acquisition, preprocessing, segmentation, morphology, feature extraction, and classification with a trained ANN model. Experiments were conducted to evaluate feature combinations, including colour and texture features. The combination of LAB colour features and texture features-contrast, correlation, energy, and homogeneity-produced the best results. Testing on 75 images achieved an average precision of 97.22%, recall of 96.67%, F1-Score of 96.80%, and accuracy of 97.33%, with an average computation time of 0.02 seconds per image. These findings indicate that the proposed method provides fast and highly accurate classification of papaya’s nutritional content, offering a practical alternative to laboratory testing. Nevertheless, the study is limited by the relatively small dataset and controlled acquisition environment. Future research should extend the dataset, incorporate deep learning approaches, and validate performance under real-world conditions to enhance robustness and generalization
Co-Authors Abdul Mansyur Abdul Muis Mappalotteng Alimuddin Syar Amelia Andi Ahmad Taufiq Andi Baso Kaswar Andi Baso Kaswar Arfah, Faizal Hanum Zalsabilah Idham Muh Fuad Zahran Firman Muhammad Fajar B