Garuda - Garba Rujukan Digital

The Influence of Leadership, Technology Acceptance and Training on Performance Muhajirin, Adi; Hapzi Ali
Dinasti International Journal of Digital Business Management Vol. 4 No. 4 (2023): Dinasti International Journal of Digital Business Management (June - July 2023)
Publisher : Dinasti Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31933/dijdbm.v4i4.1892

Literature Review Article the Effect of Leadership, Technology Acceptance and Training on Performance is a scientific article that aims to build a research hypothesis of the influence between variables that will be used in further research, within the scope of Human Resource Management science. The method of writing this Literature Review article is by library research method, which is sourced from online media such as Google Scholar, Mendeley and other academic online media. The results of this literature review article are: 1) Leadership affects performance; 2) Technology Acceptance affects performance; and 3) Training affects performance.

Integration of Deep Learning and Autoregressive Models for Marine Data Prediction Mukhlis, Mukhlis; Maulidia, Puput Yuniar; Mujib, Achmad; Muhajirin, Adi; Perdana, Alpi Surya
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 24 No 1 (2024)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i1.4032

Climate change and human activities significantly affect the dynamics of the marine environment, making accurate predictions essential for resource management and disaster mitigation. Deep learning models such as Long Short-Term Memory excel at capturing non-linear temporal patterns, while autoregressive models handle linear trends to improve prediction accuracy. This aim study predicts sea surface temperature, height, and salinity using deep learning compared to Moving Average and Autoregressive Integrated Moving Average methods. The research methods include spatial gap analysis, temporal variability modeling, and oceanographic parameter prediction. The relationship betweenparameters is analyzed using the Pearson Correlation method. The dataset is divided into 80% training and 20% test data, with prediction results compared between Long Short-Term Memory, Moving Average, and Autoregressive models. The results show that Long Short-Term Memory performs best with a Root Mean Squared Error of 0.1096 and a Mean Absolute Error of 0.0982 for salinity at 13 sample points. In contrast, Autoregressive models produce a Root Mean Squared Error of 0.193 for salinity, 0.055 for sea surface height, and 2.504 for sea surface temperature, with a correlation coefficient 0.6 between temperature and sea surface height. In conclusion, the Long Short Term Memory model excels in predicting salinity because it is able to capture complex non-linear patterns. Meanwhile, Autoregressive models are more suitable for linear data trends and explain the relationship between parameters, although their accuracy is lower in salinity prediction. This approach

Optimasi Partisipasi Pemilih pada Pemilihan Presiden di Indoensia dengan Sistem Informasi Berbasis Radio Frequency Identification (RFID) Mukhlis, Mukhlis; Muhajirin, Adi
Jurnal Pendidikan Tambusai Vol. 7 No. 2 (2023): Agustus 2023
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai, Riau, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jptam.v7i2.6308

Pemungutan suara merupakan suatu metode untuk menentukan hasil keputusan dalam kehidupan manusia agar dapat menyelesaikan suatu permasalahan. Pemilu Elekronik di terapkan pada level terendah sampai dengan level tertinggu, misalkan pada pemilihan RT, pemilihan kepala negara .Di negara demokrasi kususnya indonesia di lakukan 5 tahun sekali dalam menentukan kepala negara serta wakilnya , dana yang di keluarkan negara cukup besar dan nilainya sangat signifikan mencapai nominal tiga puluh triliun. Dengan adanya e-pemilu Elekronik diharapkan uang negara untuk pemilihan Presiden yang berjumlah Rp. 30 Triliun bisa digunakan untuk melaksanakan pemilihan presiden 2 sampai 3 periode. Sama halnya dengan pemilu Elekronik, e-pemilu Elekronik bertujuan untuk mencari jalan keluar dan menentukan hasil keputusan, tetapi proses pemilihan dilakukan secara elektronik. E-Pemilu Elekronik merupakan suatu pemilihan yang datanya disimpan, diproses dan dicatat dalam bentuk informasi secara digital. Centinkaya dan Centinkaya menambahkan bahwa “e-pemilu Elekronik refers to the use of computers or computerized pemilu Elekronik equipment to cast ballots in an election” (Cetinkaya O dan Cetinkaya D, 2007). Jadi e-pemilu Elekronik pada hakikatnya adalah “pelaksanaan pemungutan suara yang dilakukan secara digital mulai proses pendaftaran calon, pelaksanaan pemilih, penghitungan suara, dan pengiriman hasil suara”.

Integration of Deep Learning and Autoregressive Models for Marine Data Prediction Mukhlis Mukhlis; Puput Yuniar Maulidia; Achmad Mujib; Adi Muhajirin; Alpi Surya Perdana
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 1 (2024)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i1.4032

Climate change and human activities significantly affect the dynamics of the marine environment, making accurate predictions essential for resource management and disaster mitigation. Deep learning models such as Long Short-Term Memory excel at capturing non-linear temporal patterns, while autoregressive models handle linear trends to improve prediction accuracy. This aim study predicts sea surface temperature, height, and salinity using deep learning compared to Moving Average and Autoregressive Integrated Moving Average methods. The research methods include spatial gap analysis, temporal variability modeling, and oceanographic parameter prediction. The relationship betweenparameters is analyzed using the Pearson Correlation method. The dataset is divided into 80% training and 20% test data, with prediction results compared between Long Short-Term Memory, Moving Average, and Autoregressive models. The results show that Long Short-Term Memory performs best with a Root Mean Squared Error of 0.1096 and a Mean Absolute Error of 0.0982 for salinity at 13 sample points. In contrast, Autoregressive models produce a Root Mean Squared Error of 0.193 for salinity, 0.055 for sea surface height, and 2.504 for sea surface temperature, with a correlation coefficient 0.6 between temperature and sea surface height. In conclusion, the Long Short Term Memory model excels in predicting salinity because it is able to capture complex non-linear patterns. Meanwhile, Autoregressive models are more suitable for linear data trends and explain the relationship between parameters, although their accuracy is lower in salinity prediction. This approach

Klasifikasi Algoritma K-Nearest Neigbhor Untuk Memprediksi Barang Pada PT Enesis Group Adi Muhajirin; Sastro Atmojo Sasosno; Truly Wangsalegawa
Journal of Informatic and Information Security Vol. 2 No. 2 (2021): Desember 2021
Publisher : Program Studi Informatika, Fakultas Ilmu Komputer Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/g92qhz24

The company's success in maintaining its business is inseparable from the company's role in managing inventory (inventory) of goods so that it can meet the demands of customers as much as possible. During this time at PT. Enesis Group for data collection in warehouses is still carried out by keeping a manual in the book, which causes the accumulation of documents and the risk of loss or damage to documents when the item data is needed to make a report to superiors and also as a performance evaluation aterial. Often there is a lack of goods / reject goods. Do not have a computerized system. In the case of this study, the K-Nearest Neighbor method can be applied to the prediction of goods going out at the warehouse of PT. Enesis Group because it can predict the goods out correctly so that there is no shortage or excess stock of goods in the warehouse. The results of the calculation of the prediction of goods going out with the KNN method with an optimal value of K (9) are for ordering goods with a shipping distance of more than 1000 km and the expiration of goods more than 1 year, as well as for ordering goods with a distance of less than 1000 km and the expiration of goods is less from 1 year, the item is eligible to send.

Perancangan ChatBot Pendaftaran Siswa Dengan Telegram BOT Design a Chatbot for Student Registration Using Telegram BOT Harry Priambodo; Adi Muhajirin
Journal of Informatic and Information Security Vol. 3 No. 1 (2022): Juni 2022
Publisher : Program Studi Informatika, Fakultas Ilmu Komputer Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/2d0gqs45

The increasing number of Covid-19 victims is making us more and more aware of the importance of health protocols from wearing masks, maintaining distance, always washing hands, avoiding crowds, etc. It was recorded that on May 19, 2022, 6 million Indonesians were confirmed positive for Covid-19, however in the new academic year SDN Sriamur 02 still applies on-site registration, and because of the increasing level of prospective students from year to year, the school admin officers are overwhelmed, from the results of initial observations. the guardians of SDN Sriamur 02, the majority of the application services used are chat services, besides being able to be used anywhere, they are also more efficient in using internet quota, therefore the author has an idea to create a bot that runs on the Telegram Messenger application that can help in the registration of prospective students, where the development model that will be used by the author is Extreme Programming which is a software development model that tries to simplify the various stages in the development process so that it becomes more adaptive and flexible, so that the Extreme Programming (XP) method puts forward adevelopment process that is more responsive to needs. The research carried out resulted in a system that was ready to be used anytime anywhere for 24 hours and succeeded in carrying out one of the protocols in the pandemic era, namely social distancing, and a dataset of prospective students stored in the Googlesheet database.

Integration of Deep Learning and Autoregressive Models for Marine Data Prediction Mukhlis, Mukhlis; Maulidia, Puput Yuniar; Mujib, Achmad; Muhajirin, Adi; Perdana, Alpi Surya
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 1 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i1.4032

Climate change and human activities significantly affect the dynamics of the marine environment, making accurate predictions essential for resource management and disaster mitigation. Deep learning models such as Long Short-Term Memory excel at capturing non-linear temporal patterns, while autoregressive models handle linear trends to improve prediction accuracy. This aim study predicts sea surface temperature, height, and salinity using deep learning compared to Moving Average and Autoregressive Integrated Moving Average methods. The research methods include spatial gap analysis, temporal variability modeling, and oceanographic parameter prediction. The relationship betweenparameters is analyzed using the Pearson Correlation method. The dataset is divided into 80% training and 20% test data, with prediction results compared between Long Short-Term Memory, Moving Average, and Autoregressive models. The results show that Long Short-Term Memory performs best with a Root Mean Squared Error of 0.1096 and a Mean Absolute Error of 0.0982 for salinity at 13 sample points. In contrast, Autoregressive models produce a Root Mean Squared Error of 0.193 for salinity, 0.055 for sea surface height, and 2.504 for sea surface temperature, with a correlation coefficient 0.6 between temperature and sea surface height. In conclusion, the Long Short Term Memory model excels in predicting salinity because it is able to capture complex non-linear patterns. Meanwhile, Autoregressive models are more suitable for linear data trends and explain the relationship between parameters, although their accuracy is lower in salinity prediction. This approach

Pelatihan Capacity Building Sebagai Upaya Pemberdayaan Perempuan Di Kampung Muara Gembong Hutagalung, Eka Putri Christiani; Wicaksono, Wisnu; Sarasati, Budi; Muhajirin, Adi
Jurnal Psikologi Atribusi : Jurnal Pengabdian Masyarakat Vol. 1 No. 1 (2023): Dinamika Peningkatan Psikoedukasi di Masyarakat
Publisher : Fakultas Psikologi, Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/zzqyr012

Tujuan penulis dan tim penelitian memberikan sosialisasi capacity building, adalah untuk membantu meningkatkan pemahaman dan kemampuan masyarakat Kampung Sungai Labuh khususnya ibu-ibu rumah tangga di sana agar bisa memberdayakan diri sendiri untuk membantu perekonomian keluarga, karena dari hasil observasi penulis dan tim penelitian kebanyakan masyarakat di sana belum mampu mengelola suatuusaha sehingga mereka sangat bergantung pada musim dan kurangnya pemahaman masyarakat akan potensi sumber daya alam daerahnya yang melimpah. Sasaran lokasi pelaksanaan kegiatan ini bertempat di Kampung Sungai Labuh, dan lokasi berlangsungnya acara di Pondok Pesantren Fastabiqul Khairots, Jl. Kp. Sungai Labuh,Desa Pantai Harapan Jaya RT 02 RW 014, Kecamatan Muara Gembong Kabupaten Bekasi Jawa Barat. Dari lingkungan tersebut terdapat 20 orang ibu rumah tangga yang ingin berpartisipasi dalam rangkaian kegiatan riset hibah desa ini, dimana salah satu kegiatannya adalah sosialisasi dan pelatihan capacity building, yang didalamnya ada sosialisasi dan pelatihan membuat eco enzyme untuk membantu ibu-ibu rumah tangga Kampung Sungai Labuh bisa mandiri dan berdikari membantu perekonomian keluarganya. Hasil kegiatan dan manfaat penulis dan tim penelitian memberikan pelatihan capacity building adalah untuk membantu masyarakat Kampung Sungai Labuh, khususnya ibu-ibu rumah tangga agar mempunyai pemahaman dan kemampuan baru untuk modal usaha rumahan yang berguna sebagai pemasukan tambahan di keluarganya.

Klasifikasi Sentimen Opini Metaverse dari Twitter Menggunakan Algoritma Support Vector Machine Herlawati, Herlawati; Muhajirin, Adi; Izdihar, Zalfa
Jurnal Ilmiah FIFO Vol 15, No 1 (2023)
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/fifo.2023.v15i1.007

With the increasing use of Twitter, a real-time social media platform, it has become one of the places or spaces for people to express their opinions about the metaverse. Therefore, the development of a program capable of classifying tweets based on their opinions into positive, negative, and neutral categories is necessary. In conducting sentiment analysis, the Support Vector Machine (SVM) algorithm is used for classification. The results of this research, through testing using a confusion matrix, yield an accuracy rate of 0.83 or 83%, indicating the level of agreement between the model's predictions and the actual outcomes. Additionally, a precision of 0.93 or 93% is obtained, which shows the model's ability to accurately identify positive, negative, and neutral sentiments in tweets, and a recall of 0.83 or 83%, which describes the model's capability to find and classify accurately.

Penerapan Logika Fuzzy Pada Penilaian Mutu Dosen Terhadap Tri Dharma Perguruan Tinggi Adiguna, Mochamad Adhari; Muhajirin, Adi
JOIN (Jurnal Online Informatika) Vol 2 No 1 (2017)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v2i1.74

Tri Dharma Perguruan Tinggi merupakan kegiatan yang harus dilakukan oleh setiap Dosen, karena hal tersebut termasuk beban kerja Dosen sesuai peraturan Presiden No 4 Tahun 2014 RI. Dalam pelaksanaannya, Perguruan Tinggi memiliki peran penting agar dapat mendukung dan mengevaluasi kegiatan tersebut. Salah satu evaluasi yang dapat digunakan yaitu penilaian mutu Dosen terhadap beban kerja Dosen tersebut. Pada penelitian ini dirancang aplikasi untuk menerapkan logika Fuzzy untuk perhitungan nilai mutu Dosen terhadap pelaksanaan Tri Dharma Perguruan Tinggi. Latar belakang dari penelitian ini ingin mengetahui hasil yang didapat dari penerapan dan perhitungan menggunakan logika Fuzzy, juga membantu evaluasi Dosen pada bidang pengendali mutu. Pada penerapannya digunakan 27 aturan/rules untuk komposisi aturannya, himpunan Fuzzy dengan semesta dan aplikasi fungsi implikasi pada satu kondisi nilai yang selanjutnya dihitung menggunakan penalaran/inferensi dan defuzzifikasi. Batasan masalah dari penelitian ini menerapkan logika Fuzzy pada variabel pendidikan, penelitian dan pengabdian sehingga menghasilkan output yang sesuai. Adapun logika Fuzzy yang digunakan yaitu Fuzzy mamdani. Hasil pengujian pada aplikasi menunjukan 100% bekerja dengan baik, selisih nilai antara pengujian dan penalaran, hasil pengujian 2,36 lebih kecil dari hasil penalaran.

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