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All Journal Jurnal Kelautan : Indonesian Journal of Marine Science and Technology Acta Aquatica: Aquatic Sciences Journal Jurnal Riset Akuakultur KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Jurnal Mantik Jurnal Pengabdian Masyarakat: Darma Bakti Teuku Umar JPM: JURNAL PENGABDIAN MASYARAKAT International Journal of Engineering, Science and Information Technology Djtechno: Jurnal Teknologi Informasi Global Science Society Sisfo: Jurnal Ilmiah Sistem Informasi Journal of Fish Nutrition ABDIMU: Jurnal Pengabdian Muhammadiyah Jurnal Perikanan
Salamah Salamah
Program Studi Akuakultur, Fakultas Pertanian, Universitas Malikussaleh
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Transportation Other

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DATA PROCESSING TRAINING USING MICROSOFT EXCEL FOR STUDENTS OF THE FACULTY OF AGRICULTURE, MALIKUSSALEH UNIVERSITY Rachmawati Rusydi; Mainisa Mainisa; Mahdaliana Mahdaliana; Muhammad Hatta; Eva Ayuzar; Salamah Salamah
ABDIMU: Jurnal Pengabdian Muhammadiyah Vol 3, No 2 (2023)
Publisher : Universitas Muhammadiyah Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37598/abdimu.v3i2.1711

Abstract

Data processing is part of the statistics used for drawing conclusions on the data analyzed using various statistical softwares. One of the softwares for simple data processing is Microsoft Excel. However, the use of Microsoft Excel for statistical analysis by students of the Agriculture Faculty, Malikussaleh University is not optimal compared to some other software. The purpose of this training activity is to provide data processing knowledge and skills to students of the Agriculture Faculty, Malikussaleh University using Microsoft Excel on data analysis. The community service activities were carried out at the Statistics Laboratory of the Agriculture Faculty, Malikussaleh University in March 2023. The method applied was training on data analysis through data analysis microsoft excel. Evaluation of community service activities was carried out through a survey using an online questionnaire on the Google form. Data processing training activities using Microsoft Excel for students of the Faculty of Agriculture, Malikussaleh University provided increased knowledge and skills. Students assessed that the use of Microsoft Excel in data processing was important and necessary in addition to other software. Furthermore, the training activities were considered very good, in terms of delivery, materials, and discussion time.  Keywords: Data Processing; Microsoft Excel, Student, Agriculture
PENGARUH PENAMBAHAN ASTAXANTHIN PADA PAKAN KOMERSIL TERHADAP PERTUMBUHAN DAN KELANGSUNGAN HIDUP BENUR UDANG WINDU (Penaeus monodon) T. Fajar Khairullah; Suri Purnama Febri; Hanisah Hanisah; Salamah Salamah
Jurnal Perikanan Unram Vol 14 No 3 (2024): JURNAL PERIKANAN
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jp.v14i3.815

Abstract

Tiger prawns are a fishery commodity that has quite high economic value because tiger prawns taste delicious and tasty and their nutritional content is very high. The obstacles often faced in cultivation are slow growth and low survival of tiger prawn fry (Penaeus monodon), so this research aims to analyze the effect of adding astaxanthin to commercial feed on the growth and survival of tiger prawn fry (Penaeus monodon) and determine the best dose of astaxanthin. added to commercial feed can increase the growth and survival of tiger prawns (Penaeus monodon). The research method used was a Completely Randomized Design (CRD) with 4 treatments and 3 three replications. The treatments that will be carried out are: control, PA1 (150 g/ 1/2 kg feed), PA2 (250 g/ 1/2 kg feed) and PA3 (500 g/ 1/2 kg feed). Parameters observed: Absolute Length Growth, Absolute Weight Growth, Daily Growth Rate, Survival rate (SR), Feed Efficiency and Feed Conversion Ratio. The results of this research show that the best effect of Astaxanthin in commercial feed for the growth of tiger prawns is in the PA2 treatment (addition of astaxanthin at a dose of 250 g). Keywords: astaxanthin, growth, survival rate, tiger prawns
Implementasi Sistem Informasi Peramalan Single Expoenential Smoothing Dalam Melihat Kebutuhan Stok Padi di Dinas Pertanian Aceh Utara Angga Pratama; Salamah Salamah
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 2 No. 2 (2018): Sisfo: Jurnal Ilmiah Sistem Informasi, Oktober 2018
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/sisfo.v2i2.1009

Abstract

Padi merupakan suatu sektor komoditas pangan yang sangat dibutuhkan diIndonesia. Komoditas padi sangat berperan penting bagi keberlangsungankehidupan masyarakat karena beras merupakan sumber makanan pokok bagisebagian besar masyarakat di Indonesia. Ketersediaan pangan terutama padimenjadi tanggung jawab setiap pemerintah agar terjaga kestabilan harga bagimasyarakat di setiap daerah tidak terkecuali di Kabupaten Aceh Utara. Agartidak terjadi krisis pangan terutama padi di Kabupaten Aceh Utara, maka dinaspertanian perlu meramalkan ketersediaaan dan jenis padi dimasa yang akandatang. Untuk mendukung dinas Pertanian dalam mengambil kebijakan, makadiperlukan sebuah system informasi peramalan yang dapat menampung semuainformasi tentang stok jenis padi Hal ini dilakukan agar dapat memberikaninformasi kepada pihak dinas pertanian tentang beberapa jenis padi serta jumlah stok padi yang tersedia. Peramalan merupakan salah satu ilmu dalam bidang teknologi informasi yang dapat memberikan informasi pada pihak dinas pertanian sebelum mengambil kebijakan. Variabel yang digunakan pada sistem ini adalah jenis komoditi padi seperti beras delanggu dan beras IR4. Metode Double Exponential Smoothing sangat tepat digunakan ketika pola data bersifat musiman dan trend (kenaikan). Karena perilaku data jenis komoditi tersebut bersifat musiman (per-bulan) dan trend (kenaikan). Tujuan dari sistem informasiperamalan ini adalah dapat memudahkan dinas Pertanian dalam meramalkan stok kebutuhan padi dengan memperhatikan jenis padi dan jumlah yang tersedia.Kata kunci : Sistem Informasi, Least square, Peramalan, Jenis Komoditi tanamanpangan
Strategi Pemasaran Pakan Ikan Buatan Berbahan Baku Lokal Daun Kelor Di Gampong Reuleut Timur Kecamatan Muara Batu Riani Riani; Rachmawati Rusydi; Mainisa Mainisa; Salamah Salamah; Saiful Adhar
Jurnal Pengabdian Masyarakat: Darma Bakti Teuku Umar Vol 4, No 2 (2022): Juli-Desember
Publisher : Universitas Teuku Umar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35308/baktiku.v4i2.5642

Abstract

Moringa leaf-based fish feed is feed that is processed by pond farmers in Gampong Reuleut Timur, Muara Batu District, North Aceh, using Moringa leaf raw materials that have good nutritional content and are in accordance with fish needs. This feed is relatively cheap with protein content that meets the needs of fish. However, the feed has not been able to reach a wide market. This is due to the lack of knowledge of farmers about marketing strategies. The service team, through community service activities, aims to provide counseling about marketing strategies for artificial fish feed. Service activities were carried out in Gampong Reuleut Timur, Muara Batu District, North Aceh Regency. Extension activities were carried out by presenting material on the 4P marketing strategy, namely product, price, place, and promotion, and distributing questionnaires to participants to evaluate their understanding of the material. The result of this activity is the increased understanding and knowledge of partners about the 4P marketing strategy, with the hope that later it will be applied to the business that has been occupied in order to obtain maximum profit.<img 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
OPTIMALISASI TRANSFORMASI DATA SEMI-TERSTRUKTUR DAN TIDAK TERSTRUKTUR BERBASIS XML/JSON DALAM ARSITEKTUR DATA WAREHOUSE MODERN Andy Satria; Zulham Zulham; Salamah Salamah
Djtechno: Jurnal Teknologi Informasi Vol 6, No 2 (2025): Agustus
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/djtechno.v6i2.7366

Abstract

Perkembangan teknologi informasi telah memperluas tantangan dalam pengelolaan data warehouse, khususnya dalam mentransformasi data semi-terstruktur dan tidak terstruktur. Penelitian ini bertujuan untuk membandingkan efektivitas beberapa metode transformasi data dalam mengintegrasikan format data yang heterogen. Pendekatan yang digunakan berupa analisis komparatif terhadap teknik berbasis XML dan JSON, dengan indikator evaluasi mencakup akurasi, kecepatan pemrosesan, dan kompleksitas algoritmik. Hasil penelitian menunjukkan bahwa metode berbasis XML/JSON memberikan kinerja terbaik, dengan akurasi transformasi sebesar 87,5%, waktu pemrosesan tercepat 120 milidetik, serta tingkat kompleksitas yang rendah. Temuan ini menunjukkan bahwa penggunaan metode transformasi yang fleksibel dan adaptif sangat diperlukan dalam arsitektur data warehouse modern, terutama dalam menghadapi tantangan integrasi data dari berbagai sumber
PENGEMBANGAN WEBSITE SEBAGAI INTEGRASI TEKNOLOGI INFORMASI UNTUK TRANSFORMASI PENDIDIKAN DALAM OPERASIONAL PERPUSTAKAAN Andy Satria; Salamah Salamah
Djtechno: Jurnal Teknologi Informasi Vol 5, No 3 (2024): Desember
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/djtechno.v5i3.5712

Abstract

Penggunaan teknologi sistem informasi memudahkan manusia dalam memperoleh, mengelola, dan menyimpan data serta informasi secara lebih efisien. Perpustakaan di SMA Swasta Dharmawangsa, yang berlokasi di Jl. KL. Yos Sudarso No.224 Kota Medan, Sumatera Utara, masih menggunakan proses manual dalam pencatatan data buku dan pembuatan laporan. Proses manual tersebut berpotensi menyebabkan kesalahan pencatatan, laporan yang tidak akurat, dan membutuhkan waktu yang lebih dalam pencarian data yang dibutuhkan. Penelitian ini bertujuan untuk mengembangkan aplikasi perpustakaan berbasis website guna mempermudah pengelolaan data di perpustakaan sekolah. Metode penelitian yang digunakan adalah metode kualitatif dengan pengumpulan data melalui wawancara dan observasi. Sementara itu, metode pengembangan sistem menggunakan Waterfall, yang mencakup tahapan analisis kebutuhan, perancangan sistem, implementasi, integrasi, pengujian, dan evaluasi hasil. Hasil penelitian ini diharapkan mampu mengoptimalkan pengelolaan data perpustakaan secara lebih efisien dan akurat, serta mempercepat proses pencarian informasi di lingkungan sekolah.
Water Quality Monitoring and Control System for Tilapia Cultivation Based on Internet of Things Lidya Rosnita; Muhammad Ikhwani; Hafizh Al Kautsar Aidilof; Salamah Salamah; Widia Hamsi; Haris Yunanda Rangkuti
International Journal of Engineering, Science and Information Technology Vol 4, No 4 (2024)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v4i4.566

Abstract

This research analyzes the quality of water for tilapia habitat which is a type of brackish water fish that is currently widely cultivated by pond farmers. This fish is the choice because of its flexibility regarding habitat. However, despite having flexibility in terms of habitat, each harvest of tilapia that lives in a different habitat will produce tilapia with different quantity and quality. Currently, many tilapia farmers still carry out the cultivation process using traditional methods using ponds. Kuala Kerto Village, Lapang District, North Aceh is one of the locations where many tilapia fish farmers use ponds as a habitat for this fish. Not infrequently, changes in natural conditions such as rain and floods have an impact on tilapia fish ponds in this village. Thus, crop yields are very varied, often even resulting in losses. One of the reasons for this is that there is still minimal use of technology in tilapia cultivation in this village. The design of a water quality monitoring and control system for IoT-based tilapia cultivation in this research was carried out to help the problems of tilapia pond farmers. Through this research, a tool was produced in the form of a prototype IoT device that can be used to monitor and control water quality in tilapia fish ponds. This device utilizes several sensors such as turbidity sensors, ammonia sensors, salinity sensors, pH sensors, and several other sensors as data takers which will later be transmitted and displayed via a web application. Research and development of this device uses the RD method, namely research and development.
Comparative Analysis of K-Means and K-Medoids to Determine Study Programs Salamah Salamah; Dahlan Abdullah; Nurdin Nurdin
International Journal of Engineering, Science and Information Technology Vol 5, No 1 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i1.673

Abstract

Education is the main foundation for the advancement of civilization. A high level of education in society is directly proportional to the progress of that civilization. Higher education plays an important role in shaping quality human resources and contributing to community and national development. In today’s era of information and technology, data processing and analysis are key to understanding the development of study programs in higher education institutions. Clustering techniques are used to identify patterns and relationships in large and complex datasets, which are crucial in determining study programs at educational institutions. This research compares two popular clustering methods, K-Means and K-Medoids to determine study programs. The data used consists of odd semester grades of 87 students in the third-years of high school with 5 variables. The information of clusters is based on the minimum academic criteria of 18 study programs representing 7 faculties in Malikussaleh University and grouped into 5 clusters. The evaluation of clusters is conducted using the Davies-Bouldin Index (DBI). The result of the study indicate that K-Means algorithm has 5 clusters with cluster members of 31, 5, 13, 26 and 17, and a DBI value of 1,19010. Meanwhile, the K-Medoids algorithm has 5 clusters with cluster members of 33, 15, 17, 17 and 5, and a DBI value of 1,27833. Based on the DBI value, the K-Means algorithm demonstrates better cluster quality compared to the K-Medoids algorithm.
Pengaruh penggunaan probiotik pada media pemeliharaan terhadap benih maskoki (Carassius auratus) pada umur yang berbeda Vivi Julianti; Salamah Salamah; Muliani Muliani
Acta Aquatica: Aquatic Sciences Journal Vol. 3 No. 2 (2016): Acta Aquatica: Aquatic Sciences Journal
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/aa.v3i2.326

Abstract

Penelitian ini dilakukan di Laboratorium Hatchery dan Teknologi Budidaya Perairan Program Studi Budidaya perairan Fakultas Pertanian Universitas Malikussaleh, dimulai dari tanggal 27 April - 26 Mei 2015. Tujuan penelitian ini adalah mengetahui dosis probiotik yang tepat pada pemeliharaan untuk dapat menjaga kualitas air dan mengetahui umur benih yang tepat untuk dapat memanfaatkan probiotik dengan baik sehingga meningkatkan pertumbuhan dan kelangsungan hidup benih maskoki. Metode yang digunakan dalam penelitian ini dilakukan secara eksperimental dengan menggunakan Rancangan Acak Lengkap (RAL) pola faktorial dengan dua faktor. Perlakuan yang digunakan yaitu enam perlakuan dan tiga ulangan dengan pemberian probiotik 1,5 mg/L-2,0 mg/L untuk benih maskoki berumur satu dan dua bulan. Hasil penelitian menunjukkan bahwa penggunaan probiotik pada media pemeliharaan berpengaruh sangat nyata terhadap pertumbuhan berat dan kualitas air tetapi tidak berpengaruh terhadap pertumbuhan panjang, kelangsungan hidup, dan konversi pakan benih maskoki (Carassius auratus) pada umur yang berbeda. Hasil pengamatan menunjukkan bawah parameter kualitas air yang diukur selama penelitian menunjukkan kisaran yang sesuai atau masih dapat ditolerir untuk pemeliharaan benih ikan maskoki yaitu dengan kisaran suhu 27,1-27,7 °C, DO 6,0-9,0 mg/L, pH 7,2-7,5, amonia 0,03-0,12 mg/L, nitrit 0,012-0,090 mg/L, dan nitrat 0,023-3,52 mg/L. Perlakuan A2B2 dengan dosis 1,5 ml/L pada benih berumur dua bulan merupakan dosis terbaik yang menghasilkan kelangsungan hidup tertinggi sebesar 73,33 %, namun laju pertumbuhan panjang, berat, dan nilai konversi tertinggi terdapat pada perlakuan A3B2 dengan dosis 2,0 ml/L pada benih berumur dua bulan yaitu sebesar 0,39 cm, 0,52 gr, dan 0,48 gr.The research was conducted at the Laboratory of Agriculture Hatchery and Technology Aquaculture Departement Faculty of Agriculture, Malikussaleh University, started on April 27th to Mey 26th 2015. The purpose of this study is know the dosage probiotics proper in maintenance for can keep of water quality and know age larvae proper to can use probiotics well raising growth and survival seed goldfish. The research used experimental method with factorials by using complete randommized design (CRD) with two factors. Those who used the six treatment and three remedial by the provision of probiotics 1.5 mg / l-2,0 mg / l for seed maskoki mature in one and two months. The research results show that the use of probiotics in a media maintenance influential very real on the growth of heavy and water quality but has not been affecting the growth long , survival , and conversion of the feed goldfish larvae at the age of different .Our observations showed under parameter a quality of water that measured for research shows a range appropriate or can still be tolerated for the maintenance of  goldfish larvae namely by the temperature range 27,1-27,7 °C, DO 6,0-9,0 mg/L, pH 7,2-7,5, ammonia 0,03-0,12 mg/L, nitrites 0,012-0,090 mg/L, and nitrate 0,023-3,52 mg/L. Treatment with doses A2B2 1.5 mg/L on larvae was two months is doses best that produces survival highest of 73,33 %, but growth rate long, heavy, and the conversion is highest in treatment A3B2 with doses 2,0 ml / l on larvae was two months is as much as 0.39 cm, 0,52 gr, and 0,48 gr.
Pemberian probiotik pada pakan komersil dengan protein yang berbeda terhadap kinerja ikan lele (Clarias sp.) menggunakan sistem bioflok Salamah Salamah; Zulpikar Zulpikar
Acta Aquatica: Aquatic Sciences Journal Vol. 7 No. 1 (2020): Acta Aquatica: Aquatic Sciences Journal
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/aa.v7i1.2388

Abstract

Dalam usaha budidaya ikan, pakan merupakan salah satu faktor yang menentukan keberhasilan dalam budidaya. Selain itu pakan juga memberikan kontribusi yang besar terhadap biaya mencapai 60-70% dari biaya produksi. Berdasarkan hal tersebut perlu dilakukan teknik untuk meningkatkan kadar protein dari pakan, salah satunya dengan cara fermentasi. Selanjutnya limbah dari pakan juga merupakan suatu kendala dalam usaha budidaya. Untuk mengatasi permasalahan tersebut perlu dilakukan terobosan dalam sistem budidaya, salah satunya dengan menggunakan system bioflok dengan memanfaatkan bakteri heterotrofik. Selain memperbaiki kualitas air, bioflok juga menyediakan pakan secara insitu, sehingga mampu mengurangi biaya pakan dengan meningkatkan nilai efesiensi pakan. Penelitian ini bertujuan untuk menganalisis aplikasi pakan berprotein terfermentasi terhadap pertumbuhan dan konversi pakan ikan lele (Clarias sp.) menggunakan sistem bioflok. Penelitian ini dilaksanakan ditempat pembudidayaan lele di desa mesjid punteut dengan menggunakan metode Rancangan Acak Lengkap (RAL) dengan mengaplikasikan 3 perlakuan dan 3 kali ulangan sehingga diperoleh 9 satuan percobaan. Parameter yang diamati dalam penelitian ini meliputi laju pertumbuhan ikan, kelangsungan hidup dan efesiensi pakan. Hasil penelitian menunjukkan bahwa pemberian probiotik pada pakan memberikan hasil terbaik terhadap laju pertumbuhan ikan 4.95%, tingkat kelangsungan hidup 97.77% dan efesiensi pakan 98.86%.Kata kunci: bioflok; ikan lele; kinerja; probiotik In aquaculture, fish feed is one of the factors that determine success in aquaculture. Besides, the feed also contributes significantly to costs reaching 60-70% of production costs. Based on this, it is necessary to use techniques to increase the protein of the feed; one of them is by fermentation. In addition, waste from the feed is also a problem in the aquaculture business. To overcome these problems, it is necessary to make a breakthrough in the aquaculture system, one of these is using a biofloc system by utilizing heterotrophic bacteria. Besides improving water quality, biofloc also provides in situ feed, so it would reduce feed costs by increasing the value of feed efficiency. This study aims to analyze the application of fermented protein feed on growth and feed conversion of catfish (Clarias sp.) using the biofloc system. This research was carried out in the catfish culture at punteut mosque village using the Completely Randomized Design (CRD) method by applying three treatments and three replications to obtain nine experimental units. Parameters observed in this study include fish growth rate, survival and feed efficiency. The results showed that the administration of probiotics in feed gives the best results on fish growth rate of 4.95%, survival rate of 97.77% and feed efficiency of 98.86%.Keywords: biofloc; catfish; performance; probiotics
Co-Authors Andy Satria Angga Pratama Chairul Adzami Dahlan Abdullah Erlangga Erlangga Eva Ayuzar Eva Ayuzar Eva Ayuzar Fahrezza Fahrezza Fakrurrazi Fakrurrazi Hafizh Al Kautsar Aidilof Hanisah Hanisah Haris Yunanda Rangkuti Hyessica Bernardeta Lubis Lidya Rosnita Mahdaliana, Mahdaliana Mainisa Mainisa Mainisa Mainisa Mainisa Mainisa Muarif Qumar Muhammad Hatta Muhammad Ikhwani Muliani Muliani Muliani Muliani Munawwar Khalil, Munawwar Mutammimul Ula Nurdin Nurdin Prama Hartami Rachmawati Rusydi Rachmawati Rusydi Riani Riani Riani, . Riri Ezraneti Saiful Adhar Suri Purnama Febri T. Fajar Khairullah Umaruddin Usman Vivi Julianti Widia Hamsi Yusniar Yusniar Zulham Zulham Zulpikar Zulpikar Zumairi Zumairi