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PENGARUH KEPEMIMPINAN DAN LINGKUNGAN KERJA TERHADAP KINERJA KARYAWAN MELALUI MOTIVASI SEBAGAI VARIABEL INTERVENING PADA PT. STARLIFT INDONESIA 88 Saman, Muhammad; Nuraeni, Nuraeni; Hasanah, Hasanah
JURNAL MUHAMMADIYAH MANAJEMEN BISNIS Vol 5 No 1 (2024): Jurnal Muhammadiyah Manajemen Bisnis (JMMB)
Publisher : Universitas Muhammadiyah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24853/jmmb.5.1.63-78

ABSTRACTThe purpose of this study was conducted to determine and analyze the influence of leadership, work environment and motivation on employee performance at PT. Starlift Indonesia 88 which consists of Leadership, Work Environment and Motivation. The population in this study are employees of PT. Starlift Indonesia 88 with a total sample of 133 people. The method of collecting data in this study is a questionnaire and documentation. The data analysis method in this study used the Structural Equation Modeling (SEM) method and the analytical tool used in this method was the Smart-PLS software.From the results of testing the hypothesis by measuring the Outer model and Inner Model it is stated that leadership has a significant effect on motivation, work environment has a significant effect on motivation, leadership has a significant effect on employee performance, work environment has no significant effect on employee performance, employee motivation has no significant effect on employee performance , leadership through work motivation has no significant effect on employee performance, and the work environment through work motivation has no significant effect on employee performance. Keywords: leadership, environmental, motivation

Optimization of the Role of the Parent Waste Bank as a Solution to Handling Household Waste in Palangka Raya Puspita, Puspita; Saman, Muhammad; Istiqomah, Anggi Nurhidayah
Dimas: Jurnal Pemikiran Agama untuk Pemberdayaan Vol 23, No 2 (2023)
Publisher : LP2M of Institute for Research and Community Services - UIN Walisongo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21580/dms.2023.232.14294

One of the big problems experienced by cities in Indonesia is waste management. The Reduce, Reuse, Recycle (3R) waste reduction activity still has major obstacles, due to low awareness in waste management. One solution that can be implemented is through efforts to develop a Garbage Bank. The existence of a waste bank in Palangka Raya City has been implemented, but the use and utilization by the community has not been maximized. This community service aims to reveal the role of the main waste bank in handling household waste in Palangka Raya City. This type of community service is descriptive-analytic, with primary and secondary data. The results of the community service show that the existence of a main waste bank is very helpful for the surrounding community in waste management, in addition to waste management, the waste bank also provides the surrounding community as customers of the bank. In optimizing the main waste bank for Palangka Raya City, public awareness and the role of the government are needed in providing adequate equipment in the waste management process at the waste bank.

Menguji Keefektifan Metode Mask R-CNN, dan Metode Keypoint R-CNN dalam Deteksi Objek Citra. Darmawan, Faris Eka; Magfiroh, Siti Khoerotul; Hanifah, Mutiara Khansa; Saman, Muhammad
JURNAL KOMPUTER DAN TEKNOLOGI INFORMASI Vol 2, No 2 (2024): Implementasi Sistem Cerdas
Publisher : Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jkti.v2i2.14084

Objek Detection merupakan tantangan dalam bidang computer vision yang mendukung aplikasi seperti pengenalan objek, pengenalan pola, dan analisis citra medis. Dalam penelitian ini, kami membandingkan kinerja dua metode utama dalam deteksi objek: Mask R-CNN, dan keypoint R-CNN.Metode R-CNN didasarkan pada pembuatan proposal wilayah menggunakan jaringan proposal wilayah (RPN), yang kemudian diproses oleh jaringan konvolusional untuk klasifikasi dan regresi. Oleh karena itu, Mask R-CNN mengintegrasikan kemampuan segmentasi instan dengan memberikan pemahaman yang lebih mendalam tentang objek. Di sisi lain, Keypoint R-CNN menambahkan dimensi pada deteksi objek dengan menentukan titik kunci atau landmark pada objek. Dimana dalam objek detection menggunakan sekitar 5000 gambar untuk implementasi metode Mask R-CNN dan Keypoint R-CNN.Evaluasi didasarkan pada akurasi deteksi, kecepatan eksekusi, dan kebutuhan sumber daya komputasi.

Optimization of the Role of the Parent Waste Bank as a Solution to Handling Household Waste in Palangka Raya Puspita, Puspita; Saman, Muhammad; Istiqomah, Anggi Nurhidayah
Dimas: Jurnal Pemikiran Agama untuk Pemberdayaan Vol. 23 No. 2 (2023)
Publisher : LP2M of Institute for Research and Community Services - UIN Walisongo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21580/dms.2023.232.14294

One of the big problems experienced by cities in Indonesia is waste management. The Reduce, Reuse, Recycle (3R) waste reduction activity still has major obstacles, due to low awareness in waste management. One solution that can be implemented is through efforts to develop a Garbage Bank. The existence of a waste bank in Palangka Raya City has been implemented, but the use and utilization by the community has not been maximized. This community service aims to reveal the role of the main waste bank in handling household waste in Palangka Raya City. This type of community service is descriptive-analytic, with primary and secondary data. The results of the community service show that the existence of a main waste bank is very helpful for the surrounding community in waste management, in addition to waste management, the waste bank also provides the surrounding community as customers of the bank. In optimizing the main waste bank for Palangka Raya City, public awareness and the role of the government are needed in providing adequate equipment in the waste management process at the waste bank.

Hyperparameter optimization of convolutional neural network using grey wolf optimization for facial emotion recognition Munsarif, Muhammad; Saman, Muhammad; Ernawati, Ernawati; Santosa, Budi
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 2: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i2.pp898-906

Facial emotion recognition (FER) is a challenging task in computer vision with wide applications in areas such as human-computer interaction, security, and healthcare. To improve the performance of convolutional neural networks (CNN) in FER, a novel approach combining CNN with grey wolf optimization (GWO) was proposed to optimize key hyperparameters. The CNN-GWO model was fine-tuned by adjusting hyperparameters such as the number of convolutional layers, kernel size, number of filters, and learning rate. This model was evaluated using the CK+ dataset and achieved an accuracy of 90.97%, demonstrating its competitive performance compared to existing methods. The optimized hyperparameters included three convolutional layers, 35 filters, a kernel size of 5, a learning rate of 0.045990, a dropout rate of 0.4988, and a max pooling size of 3. These results confirm that GWO is effective in optimizing CNN for FER tasks, providing an efficient solution to enhance model accuracy. This approach shows promising potential for future FER applications, highlighting GWO as a valuable optimization technique for CNN architectures.

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