Article Per Year (5 Year)
p-Index From 2021 - 2026
3.321
P-Index
This Author published in this journals
All Journal Jurnal Ilmu Pertanian Indonesia TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Keteknikan Pertanian Tropis dan Biosistem agriTECH Journal of Applied Geospatial Information PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Edukasi Islami: Jurnal Pendidikan Islam EDUKATIF : JURNAL ILMU PENDIDIKAN Building of Informatics, Technology and Science Jurnal Teknik Sipil dan Teknologi Konstruksi Abdimasku : Jurnal Pengabdian Masyarakat jurnal syntax admiration JURNAL PENGABDIAN AGRO AND MARINE INDUSTRY Jurnal Ilmiah Mahasiswa Pertanian Jurnal Manajemen Sistem Informasi Rona Teknik Pertanian Inovasi Teknologi Masyarakat
Fachruddin Fachruddin
Universitas Teuku Umar
Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering 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 Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Transportation Other

Published : 28 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 28 Documents
Search

 1   2   3 
Analisis Minat Konsumen pada Warung Kopi di Kota Banda Aceh Rahmat Fadhil; Sayed Mahdi; Fachruddin Fachruddin; Putra Bahrumi
Jurnal Ilmu Pertanian Indonesia Vol. 28 No. 1 (2023): Jurnal Ilmu Pertanian Indonesia
Publisher : Institut Pertanian Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18343/jipi.28.1.103

Abstract

The number of businesses like coffee shops (warkop) in Banda Aceh City makes every business actor required to implement the right strategy in capturing the hearts of consumers so that they can continue to visit that the company's income can increase. Therefore, this study aims to analyze consumer interest in choosing warkop in Banda Aceh City so that business actors can use the information obtained in making decisions to be applied to their business. The observation locations were carried out in five coffee shops. Based on the results of the study, several factors become a reference for consumers in choosing the warkop to visit, including location, place, price, internet, parking facilities, and mushalla. However, in some coffee shops, there are also problems in providing quality services, such as delays in the handling process, slight menu variations, the noise of places, parking lots, and less strategic locations. Thus, if the problem is not addressed properly, it is estimated that it can have an impact on the nature of the next consumer. With a soft systems methodology (SSM) approach, these problems can be overcome. SSM can build a conceptual model that can formulate a strategy for warkop business actors in Banda Aceh City. SSM method results in two suggested recommendation activities. First, technical recommendations, namely that business actors must make efforts in improving their companies, such as determining strategic locations, setting competitive prices, providing adequate parking space, and fixing various other factors. Second, policy recommendations include establishing special SOPs, developing human service resources, incorporating local wisdom, and innovating. So applying these two activity recommendations can provide answers to problems. Keywords: coffee shop, conceptual model, consumer interest, soft systems methodology
EVALUASI SIMPANGAN ANTAR LANTAI (INTER STORY DRIFT) PADA GEDUNG BERTINGKAT DENGAN METODE RIWAYAT WAKTU Fitry Hasdanita; Delfian Masrura; Fachruddin Fachruddin
Jurnal Teknik Sipil dan Teknologi Konstruksi Vol 9, No 1 (2023): Jurnal Teknik Sipil dan Teknologi Konstruksi
Publisher : Universitas Teuku Umar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35308/jts-utu.v9i1.7239

Abstract

Aceh salah satu provinsi paling barat yang berada pada zona tektonik yang sangat aktif karena ada sembilan lempeng kecil lainnya yang membentuk jalur komplek selain tiga lempeng besar dunia. Peristiwa gempa yang terjadi di Aceh secara horizontal dan vertikal terjadi pada 26 Desember 2004 sebesar 9,2 Mw, 11 April 2012 dengan 8,6 Mw, 23 januari 2013 dengan 6,1 Mw dan Desember 2016 yang menyebabkan kerusakan serius pada bangunan dengan berbagai tipe pola keruntuhan. Ini merupakan faktor terpenting untuk evaluasi kerusakan bangunan dan desain seismik jika terjadi gempa. Simpangan antar lantai (inter story drift) menjadi faktor utama kerusakan bangunan akibat gempa. Nilai simpangan pada bangunan juga sebagai acuan untuk menentukan tingkat kerusakan pada suatu bangunan akibat gempa berdasarkan perencanaan berbasis kinerja. Penelitian ini bertujuan untuk menganalisis simpangan antar lantai (inter story drift) pada bangunan bertingkat untuk mengetahu tingkat kerusakan bangunan akibat gempa. Hal ini bertujuan untuk mengantisipasi atau melakukan upaya mitigasi pada saat terjadi gempa sehingga menguruangi kerugian materian dan korban jiwa. Hasil penelitian menunjukkan simpangan antar lantai maksimum sebesar 0.086 m akibat riwayat gempa Irpinia pada lantai 2 dalam arah X. Simpangan antar lantai maksimum arah Y sebesar 0,056 akibat gempa Irpinia. Nilai simpangan antar lantai maksimum memenuhi persyaratan SNI-1726-2019 tentang Tata Cara Perencaan Ketahanan Gempa Untuk Struktur Banguanan Geudng dan Non Gedung yaitu 0,548 m. Sehingga Gedung DPRA Kota Banda Aceh aman terhadap kegagalan simpangan antar lantai.<img src="data:image/png;base64,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
Entrepreneurship Education Management In Realizing The Independence Of Boarding Schools In Langkat Ahmad Zaki; Fachruddin Fachruddin; Sahkholid Nasution
Edukasi Islami : Jurnal Pendidikan Islam Vol 12, No 01 (2023): Edukasi Islami: Jurnal Pendidikan Islam
Publisher : Sekolah Tinggi Agama Islam Al Hidayah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30868/ei.v12i01.4552

Abstract

Every educational institution, including Islamic boarding schools, is facing challenges that arise along with the progress of the times, namely the modernization of education. Every educational institution is required to have a good management system, so that it is able to plan, organize, implement, control and evaluate its own educational institutions. Islamic boarding schools as educational institutions that have been synonymous with conventional management systems and do not keep up with the times, feel the need to make several changes in order to become independent institutions, especially from the aspect of economic independence. One alternative that can be chosen by institutions to achieve institutional independence is entrepreneurship education. Islamic boarding schools that implement entrepreneurship education are Darussaadah Islamic Boarding School Pangkalan Susu and Al-Habib Sei Lepan Islamic Boarding School. This study aims to analyze and find, 1). entrepreneurship education strategy in realizing the independence of Islamic boarding schools, 2). implementation of entrepreneurship education strategies in realizing the independence of Islamic boarding schools 3). implications of entrepreneurship education strategies in realizing the independence of Islamic boarding schools. This research uses a qualitative approach with a multi-site study type. The data collection technique uses in-depth interviews, participant observation and documentation. Data analysis used the Myles Huberman model of data reduction, data presentation, verification and drawing conclusions. Data is analyzed from single site and cross site data. Test the validity of the data through credibility, transferability, dependability and confirmability. The research results show 1). The entrepreneurship education strategy used is student engagement in each stage of entrepreneurial activity and designing meaningful learning, 2). the implementation of entrepreneurship education strategies at Islamic boarding schools uses peer tutorial, trial and error methods and students as entrepreneurship education mentors also use a prophetic approach, namely not basing entrepreneurial activities solely on obtaining financial benefits, but also for spiritual motives, 3). the implications of entrepreneurship education in Islamic boarding schools are increasing economic independence and institutional management as well as increasing the independence of students. The formal finding of this study is that entrepreneurship education is based on the development of aspects of entrepreneurial knowledge (knowledge), entrepreneurial skills (skills), attitudes (attitudes), and individual and environmental spirituality (spirituality).
Evaluasi Kesesuaian Lahan pada Tanaman Kopi Arabika (Coffea arabica L.) Organik Menggunakan Sistem Informasi Geografis (SIG) di Kecamatan Pegasing Kabupaten Aceh Tengah Ghalib Auliansyah; Fachruddin Fachruddin; Yuswar Yunus
Jurnal Ilmiah Mahasiswa Pertanian Vol 4, No 2 (2019): Mei 2019
Publisher : Fakultas Pertanian, Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1036.256 KB) | DOI: 10.17969/jimfp.v4i2.10911

Abstract

Abstrak.  Komoditas kopi identik dengan kehidupan masyarakat Aceh Tengah, karena sebagian besar penduduk di wilayah dataran tinggi ini menggantungkan hidupnya dari komoditas kopi. Evaluasi kesesuaian lahan pada tanaman kopi Arabika organik sangat penting untuk mengoptimalkan produktivitas tanaman tersebut. Hasil penelitian menunjukkan kelas kesesuaian lahan aktual di Kecamatan Pegasing dengan analisis lahan seluas 7.800,69 ha adalah sangat sesuai (S1) seluas 2.851,28 ha (36,55%), cukup sesuai (S2) 3.616,56 ha (46,36%) dan tidak sesuai (N) seluas 1.332,85 ha (18,72%) dengan faktor pembatas terberat ketersediaan air (wa) pada semua SPL, resistensi hara (nr) pada SPL 1, 3 dan 4 serta kemiringan lereng pada semua SPL.Evaluation of Land Suitability on Organic Arabica  Coffee Plants (Coffea arabica L.) Using Geographic Information Systems (GIS) in Pegasing District Middle of Aceh RegencyAbstract. Coffee commodities are identical to the life of the people of Middle Aceh, because most of the population in this highland region depends on the commodity of coffee. Evaluation of land suitability in organic Arabica coffee plants is very important to optimize the productivity of these crops. The results showed the actual land suitability class in Pegasing District with an analysis of an area of 7,800.69 ha was very suitable (S1) covering an area of 2.851,28 ha (36,55%), quite suitable (S2) 3.616,56 ha (46,36%) and incompatible (N) covering an area of 1,332.85 ha (18.72%) with the heaviest limiting factor of water availability (wa) in all SPL, nutrient resistance (nr) in SPL 1, 3 and 4 and slope slope in all SPL.
Pengembangan Klasifikasi Berbagai Jenis Beras Dengan Menggunkan NIRS Metode PCA (Principal Component Analysis) Mufazzal Assayuti; Fachruddin Fachruddin; Zulfahrizal Zulfahrizal
Jurnal Ilmiah Mahasiswa Pertanian Vol 5, No 1 (2020): Februari 2020
Publisher : Fakultas Pertanian, Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1141.895 KB) | DOI: 10.17969/jimfp.v5i1.13627

Abstract

Abstrak. Tujuan dari penelitian ini adalah membangun model pendugaan keaslian beras Aceh menggunakan metode Principal Component Analysis (PCA). Penelitian ini menggunakan beras Sigupai, beras Tangse, beras IR-64, beras Thailand, dan beras Vietnam. Jumlah sampel yang digunakan pada penelitian ini adalah 110 sampel. Spektrum yang diambil pada penelitian ini berasal dari beras murni tanpa pencampuran dan beras dengan pencampuran 75% dan 25% untuk masing-masing beras Aceh dan beras BULOG. Pengambilan spektrum beras menggunakan Self developed FT-IR IPTEK T-1516. Hasil yang didapat pada penelitian ini yaitu: (1) NIRS dengan metode PCA mampu membedakan beras Aceh dengan beras BULOG baik dalam keadaan tanpa pencampuran maupun dengan pencampuran. (2) Analisis spektrum NIRS beras menunjukkan keberadaan kandungan karbohidrat pada panjang gelombang 4297cm-1 – 4462 cm-1. Protein terletak pada panjang gelombang 4698 cm-1  - 4879 cm-1.  Kadar air terletak pada panjang gelombang 5087 cm-1 –  5188 cm-1. Puncak gelombang 5708 cm-1 – 5805 cm-1  dan 8196 cm-1 – 8389 cm-1  menunjukkan senyawa kimia lemak. Panjang gelombang 6827 cm-1 – 6969 cm-1 menunjukkan senyawa kimia protein dan kadar air.Development classification of various types rice using NIRS PCA (Principal Component Analysis) methodAbstract. The purpose of this research is to develop a model for estimating the authenticity of Aceh rice using the Principal Component Analysis (PCA) method. This research uses Sigupai rice, Tangse rice, IR-64 rice, Thai rice, and Vietnamese rice. The number of samples used in this study was 110 samples. The spectrum taken in this study was 100% of each rice, and mixing of 75% and 25% rice between Aceh rice and BULOG rice. Intake of rice spectrum using Self-developed FT-IR IPTEK T-1516. The results obtained in this study are: (1) NIRS with PCA method is able to group Aceh rice with BULOG rice both in a state without mixing or by mixing. (2) Analysis of the rice NIRS spectrum showed the presence of carbohydrate content at wavelengths of 4297cm-1 - 4462 cm-1. Protein content at wavelength 4698 cm-1 - 4879 cm-1. Water content at a wavelength of 5087 cm-1 - 5188 cm-1. Wavelength of 5708 cm-1 - 5805 cm-1 and 8196 cm-1 - 8389 cm- 1 shows the fat content. Wavelength 6827 cm-1 - 6969 cm-1 shows the protein and water content.
Karakteristik Pengering Efek Rumah Kaca Tipe Terowongan Terhadap Kualitas Minyak Nilam Riski Satria; Fachruddin Fachruddin; Diswandi Nurba
Jurnal Ilmiah Mahasiswa Pertanian Vol 5, No 1 (2020): Februari 2020
Publisher : Fakultas Pertanian, Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (544.682 KB) | DOI: 10.17969/jimfp.v5i1.13791

Abstract

Abstrak. Minyak atsiri adalah salah satu minyak yang dihasilkan dari bagian batang, kulit, daun, akar, bunga dan berbagai bagian tumbuhan yang lain dengan proses penyulingan. Salah satu minyak atsiri adalah minyak nilam, dalam dunia perdagangan minyak atsiri dikenal dengan nama Patchouly Oil. Walaupun tanaman nilam telah dibudidayakan  selama hampir 100 tahun, namun sampai sekarang masih banyak teknologi pengolahan masyarakat masih konvensional sehingga kualitas minyak yang dihasilkan masih rendah. Penelitian ini menggunakan metode pengeringan efek rumah kaca tipe terowongan dan pengeringan konvensional. Analisis kualitas minyak nilam yang diamati meliputi kadar air nilam, rendemen, warna, bobot jenis, indeks bias, dan kelarutan dalam etanol 90%. Hasil penelitian menunjukkan bahwa kadar air nilam hasil pengeringan efek rumah kaca hanya membutuhkan waktu selama 1 hari untuk mengeringkan nilam dan menghasilkan kadar air rata-rata yaitu 22,45%, sedangkan pengeringan konvensional rata-rata kadar air yaitu 22,93% dan memerlukan waktu 2 hari untuk mengeringkan nilam. Hasil Rendemen dengan pengeringan efek rumah kaca menghasilkan rata-rata rendemen yaitu 0,40% sedangkan pengeringan konvensional menghasilkan rata-rata rendemen yaitu 0,39%. Warna minyak nilam dengan pengeringan konvensional dan pengeringan efek rumah kaca menghasilkan warna coklat kemerahan. Bobot jenis minyak nilam pengeringan efek rumah kaca rata-rata 0,966 sedangkan pengeringan konvensional rata-rata 0,954. Hasil indeks bias minyak nilam pengeringan efek rumah kaca yaitu sebesar 1,509 sedangkan pengeringan konvensional sebesar 1,508. Kelarutan dalam etanol 90% minyak nilam  pengeringan efek rumah kaca lebih baik karena jernih rata-rata pada larutan 1:9, sedangkan pengeringan konvensional jernih rata-rata pada larutan 1:10, dimana minyak nilam yang mudah larut dalam etanol 90% maka kualitas minyak nilam semakin baik. Berdasarkan kadar air dan kualitas minyak nilam seperti warna, bobot jenis, indeks bias dan kelarutan dalam etanol 90% merupakan pengeringan efek rumah kaca lebih baik secara kuantitas dan kualitas dibandingkan dengan pengeringan konvensional.Characteristic of tunnel type dryer of greenhouse effect on the quality of patchouli oil Abstract. Essential oil is the type of oil derived from stem, bark, leaves, root, flower, and other parts of plant through the process of distillation. One type of essential oil is Patchouli oil which is known as patchouli oil in the trading world. Even though patchouli plants have been cultivated for almost 100 years, the technology used to process the oil is still conventional that cause the oil quality to be low. This research utilized two methods which are greenhouse effect drying and conventional drying. The analysis of oil quality was done by observing the oil moisture, yield, color, specific gravity, refractive index, and its solubility in 90% ethanol. The result shows that the patchouli moisture by using the greenhouse effect drying needs one day to dry patchouli leaves that later produces moisture at the average of 22.45%. By using conventional drying, it took 2 days to dry that resulted in the moisture at the average of 22.93%. The result of oil yield out of the greenhouse effect drying is 0.40% in average, while by using the conventional drying the oil yielding resulted in 0.39% in average. As for the color of patchouli oil by using both the greenhouse effect drying and conventional drying result in reddish brown. The average specific gravity of patchouli oil by using the greenhouse effect drying is 0.966 at the average, while it resulted in the average of 0.954 by using conventional drying. The refractive index of patchouli oil by using greenhouse effect drying is at 1.509, while by using conventional drying is at 1.508. The solubility of patchouli oil in ethanol 90% ethanol using greenhouse effect drying is better with the ration of 1:9 to solution. However, the solubility of patchouli oil in 90% ethanol using conventional drying in ratio is 1:10. This means that the more soluble oil on 90% ethanol, the better the quality of the patchouli oil. Based on the moisture and patchouli oil quality like its color, specific gravity, refractive index, and solubility in 90% ethanol, it shows that greenhouse effect drying is better in quantity and quality than conventional drying.
Klasifikasi Kesesuaian Lahan Tanaman Kedelai di Kabupaten Aceh Tengah Berbasis Sistem Informasi Geografis (SIG) Anna Sabila; Fachruddin Fachruddin; Muhammad Yasar
Jurnal Ilmiah Mahasiswa Pertanian Vol 4, No 2 (2019): Mei 2019
Publisher : Fakultas Pertanian, Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1454.954 KB) | DOI: 10.17969/jimfp.v4i2.10941

Abstract

Abstrak. Ketersediaan kedelai sangat penting sebagai bahan pangan dan non pangan di Indonesia, sehingga menyebabkan kebutuhan akan kedelai semakin meningkat. Namun produksi kedelai nasional tidak mampu memenuhi kebutuhan kedelai itu sendiri. Akibatnya pemerintah mulai mencari solusi untuk menanggulangi hal tersebut, salah satunya yaitu pengembangan lahan alternatif. Pengembangan lahan alternatif dapat diwujudkan melalui kesesuian lahan. Penelitian klasifikasi kesesuaian lahan ini bertujuan untuk mengetahui karakteristik dan mengklasifikasikan kesesuaian lahan tanaman kedelai di Kabupaten Aceh Tengah menggunakan Sistem Informasi Geografis (SIG). Metode pengumpulan data yang digunakan adalah dokumentasi, observasi dan uji laboratorium. Analisis kesesuaian lahan pada penelitian ini menggunakan metode overlay dan metode matching. Metode overlay yaitu penanganan data secara digital dengan menggabungan beberapa peta seperti peta penggunaan lahan, peta kemiringan lereng dan peta jenis tanah. Dari hasil overlay diperoleh 13 (tiga belas) Satuan Peta Lahan (SPL) dengan nilai karakteristik yang berbeda setiap satuan lahan. Sedangkan Metode matching digunakan untuk membandingkan karakteristik lahan SPL dengan kriteria kelas kesesuaian lahan tanaman kedelai. Hasil matching menunjukkan bahwa Kabupaten Aceh Tengah tergolong dalam kelas kesesuaian lahan S3 (Sesuai Marginal) dengan luas total 182,78 ha atau 61,78%, dan kelas kesesuaian lahan N (tidak sesuai) dengan luas total 89,29 ha atau 32,82%. Classification of Soybean Land Suitability at Central Aceh Regency Based on Geographic Information System (GIS)Abstract. Soybean availability is very important as food and non-food ingredients in Indonesia, thus causing the needs of soybeans to grow more and more. But the production of national soybeans was unable to fulfil the needs of soybeans itself. As a result, the government began searching for solutions to cope with such thing, one of them is alternative land development. Alternative land development can be realized through land suitability. The aims of this land suitability classification research were to know the characteristics and classify the soybean land suitability at Central Aceh Regency using Geographic Information System (GIS). The methods of data collection that used were documentation, observation, and laboratory tests. Analysis land suitability in this research using overlay and matching method. Overlay method is digital data processes by combining some maps such as land use map, land slope map, and land type map. From the overlay results obtained 13 (thirteen) Land Map Unit (LMU) with different characteristic values for each land unit. While the matching method is used to compare the characteristics of land LMU with the criteria of soybean land suitability class. The matching results showed that Central Aceh Regency is classified as the land suitability class S3 (marginally suitable) for planting soybean with total area of 182.78 ha or 61.78%, and land suitability class N (not suitable) with a total area of 89.29 ha or 32, 82%. 
Analisis Stabilitas Dinding Penahan Tanah (Studi Kasus: Desa Mekarjaya, Kecamatan Ciomas, Kabupaten Bogor) Agus Dermawan; Syaiful Syaiful; Alimuddin Alimuddin; Fachruddin Fachruddin
Rona Teknik Pertanian Vol 15, No 2 (2022): Volume No. 15, No. 2, Oktober 2022
Publisher : Department of Agricultural Engineering, Syiah Kuala University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17969/rtp.v15i2.27778

Abstract

Abstrak.Dinding penahan tanah berfungsi untuk menahan tanah serta mencegahnya dari bahaya kelongsoran. Dinding penahan tanah merupakan dinding yang digunakan untuk menahan beban tanah secara vertikal ataupun terhadap kemiringan tertentu. Untuk meminimalisir kondisi tersebut, perlu dihitung dan direncanakan kestabilan dari struktur pada dinding penahan tanah agar mampu menahan beban dari tanah dan pengaruh beban luar. Kondisi di lapangan terdapat kerusakan pada bagian struktur dinding penahan tanah, dengan bagian dasarnya mengalami penggerusan sehingga perlu dilakukannya analisis stabilitas dinding penahan tanah. Tujuan penelitian ini adalah untuk menganalisis kondisi dinding penahan tanah terhadap stabilitas guling, geser dan kapasitas daya dukung tanah dan merencanakan desain dinding penahan tanah. Metode yang digunakan adalah Metode Rankine, Schmertmann dan Nottingham. Hasil analisis menunjukan stabilitas terhadap guling Fs = 1,55 1,5 (aman) stabilitas terhadap geser Fs = 2,51 1,5 (aman), dan analisis stabilitas daya dukung tanah didapat qtoe = 31,3953 kN/m2 Qall 2501,3841 kN/m2 (aman) untuk tegangan q hell 1,43 kN/m² 0 (lebih dari 0,hasil aman). Rencana desain dinding penahan tanah yang direncanakan dengan tinggi (H)= 4,3 m, lebar atas (ba)= 0,4 m, lebar bawah (bb)= 3,5 m, tebal kaki tumit (D)= 0,8 m, dan lebar plat dinding penahan tanah (D)= 0,8 m.Retaining Wall Stability Analysis (Case Study: Mekarjaya Village, Ciomas District, Bogor Regency)Abstract. Retaining walls function to hold the soil and prevent it from sliding. Retaining walls are walls that are used to withstand soil loads vertically or against a certain slope. To minimize these conditions, it is necessary to calculate and plan the stability of the structure on the retaining wall in order to be able to withstand the load from the soil and the influence of external loads. Conditions in the field there is damage to the retaining wall structure, with the bottom part being eroded so it is necessary to analyze the stability of the retaining wall. The purpose of this study was to analyze the condition of the retaining wall, the stability of overturning, shear, and the bearing capacity of the soil, and to plan the design of the retaining wall. This research uses the Method of Rankine, Schmertmann dan Nottingham. The results of the analysis showed that the stability against overturning Fs = 1.55 1.5 (safe) stability against shear Fs = 2.51 1.5 (safe), and the stability analysis of the bearing capacity of the soil obtained qtoe = 31.3953 kN/m2 Qall 2501.3841 kN/m2 (safe) for voltage q hell 1.43 kN/m² 0 (more than 0, safe result). The design plan of the retaining wall is planned with height (H) = 4.3 m, top width (ba) = 0.4 m, bottom width (bb) = 3.5 m, heel thickness (D) = 0.8 m, and the width of the retaining wall plate (D) = 0.8 m.
Pengaruh Pemberian Bahan Organik Dan Kapur Terhadap Kapasitas Kerja Dan Efisiensi Traktor Pada Lahan Kering Zulfakri Zulfakri; Fachruddin Fachruddin; Angga Defrian
Rona Teknik Pertanian Vol 12, No 2 (2019): Volume 12, No. 2, Oktober 2019
Publisher : Department of Agricultural Engineering, Syiah Kuala University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17969/rtp.v12i2.15452

Abstract

Abstrak. Penelitian ini bertujuan untuk mengetahui pengaruh pemberian bahan organik dan kapur berbagai dosis terhadap kapasitas kerja dan efisiensi traktor pada lahan kering. Penelitian ini menggunakan rancangan acak kelompok (RAK) faktorial yang terdiri dari 2 (dua) faktor yaitu bahan organik pada taraf 0, 6, dan 12 ton/ha sedangkan kapur pada taraf 0, 0,8 dan 1,6 ton/ha dengan 3 (tiga) kali pengulangan. Hasil penelitian menunjukkan bahwa Perlakuan bahan organik 12 ton/ha menghasilkan kapasitas lapang, efisiensi lebih tinggi serta slip roda dan kebisingan traktor yang lebih rendah dibandingkan dengan tanpa pemberian bahan organik.The Influence Of  Organic And Lime Materials On Working Capacity And Efficiency Of Tractors In Dry Land Abstract. This study aims to determine the effect of giving organic material and various doses of lime on the working capacity and efficiency of tractors on dry land. This study used a factorial randomized block design (RCBD) consisting of 2 (two) factors, namely organic matter at levels 0, 6, and 12 tons/ha while lime at levels 0, 0.8 and 1.6 tons/ha with 3 (three) repetitions. The results showed that the treatment of 12 tons/ha of organic material resulted in roomy capacity, higher efficiency and lower wheel slip and tractor noise compared to without the provision of organic material. 
Analisis Neraca Air dan Kebutuhan Air Tanaman Jagung (Zea Mays L.) Berdasarkan Fase Pertumbuhan Di Kota Tarakan Sudirman Sirait; Linda Aprilia; Fachruddin Fachruddin
Rona Teknik Pertanian Vol 13, No 1 (2020): Volume 13, No. 1, April 2020
Publisher : Department of Agricultural Engineering, Syiah Kuala University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17969/rtp.v13i1.15856

Abstract

Abstrak. Salah satu upaya peningkatan produktivitas tanaman jagung adalah penyediaan air yang cukup untuk pertumbuhan tanaman. Tanaman jagung sangat membutuhkan air dalam proses pertumbuhan dan perkembangannya. Ketepatan pemberian air sesuai dengan fase pertumbuhan tanaman jagung sangat berpengaruh terhadap produksi. Kelebihan dan kekurangan air akan mengakibatkan tanaman jagung mengalami penurunan dalam proses pertumbuhan dan produksinya. Penelitian ini bertujuan untuk menganalisis neraca air dan kebutuhan air pada tanaman jagung berdasarkan fase pertumbuhan di wilayah Kota Tarakan. Penelitian ini dibagi dalam beberapa tahapan yaitu pengumpulan data iklim bulanan periode 2008-2017, analisis neraca air dengan metode Thornthwaite-Mather, dan analisis kebutuhan air tanaman jagung berdasarkan fase pertumbuhan. Hasil penelitian menunjukan bahwa (1) Wilayah Kota Tarakan memiliki curah hujan andalan 3497,68 mm/tahun. (2) Total surplus 2997,84 mm/tahun, limpasan 1630,31 mm/tahun, dan pengisian air tanah 1367,53 mm/tahun. (3) Selama satu periode penanaman tanaman jagung rata-rata membutuhkan air sebesar 256,55 mm. (4) Total evapotranspirasi tanaman jagung selama 4 periode penanaman sebesar 1026.18 mm/tahun dan memiliki ketersediaan air yang sangat cukup serta setiap bulannya memiliki nilai surplus sepanjang tahun.Analysis of Water Balance and Crop Water Requirements of Corn (Zea mays L.) Based on Growth Phases in Tarakan CityAbstract. The effort to increase the productivity of corn plants is the provision of sufficient water for plant growth.  Corn is a plant that needs water for the process of growth and development. The accuracy of the water supply following the growth phase of corn plants is very influential in production. Excess and deficiency of water will cause corn plants to decrease in the process of growth and production. This study aims to analyze the water balance and crop water requirements in corn-based on the growth phase in the City of Tarakan. This research was divided into several stages, namely the collection of monthly climate data for the period 2008-2017, analysis of water balance by using Thornthwaite-Mather method, and analysis of corn crop water requirements based on the growth phase. The results showed that (1) the Tarakan City area had a rainfall of 3497.68 mm/year. (2) The rainfall surplus is 2997.84 mm/year, runoff 1630.31 mm/year, and groundwater recharge 1367.53 mm/year. (3) During the one planting period, corn plants require an average of 256.55 mm of water. (4) The consumptive use (ETc) of corn plants during the 4 planting periods is 1026.18 mm/year and has a very adequate water supply and every month has more value throughout the year.
Co-Authors AFZAL, MUHAMMAD Agus Dermawan Agustiar Agustiar Ahmad Zaki Alimuddin Alimuddin Alimuddin Alimuddin Alimuddin Alimuddin, Alimuddin Amiruddin Amiruddin Anna Sabila April Lidan Azi Akbar, Ahmad Cut Handriyani Cut Suciatina Silvia Defrian, Angga Delfian Masrura Dewi Sri Jayanti Diswandi Nurba, Diswandi Edison Edison Errissya Rasywir Erwinsah Putra Fadilah Khairani Feril Hariati Fitry Hasdanita Gebrina Rizki Gema Irhamdhika Ghalib Auliansyah Hafizd Arwaa Marden Handriyani, Cut Harsyah Agustin Ichwana Ramli Ichwana Ramli Indah Junidar Irawan, Beni Isfanda, Isfanda Istoningtyas, Marrylinteri Jasmi Jasmi Jayanto, Anthony LINDA APRILIA Mardhiana Mardhiana Maulidia, Vina Meylis Safriani Mufazzal Assayuti Muhamad Lutfi Muhamad Rizki Muhammad Ardiansyah Muhammad Ikhsan Muhammad Reza Aulia Muhammad Yasar Nur Indah Mansyur Nurul Fadhilah Pahlevi, M. Riza Pareza Alam Jusia Prima Eka Syam putri Putra Bahrumi Rahmat Fadhil Riski Satria Rizki, Gebrina Sabila Mujahidah Alkhayr Sahkholid Nasution Santoso, Dwi Sanusi Sanusi Sanusi Sanusi Saparudin, Saparudin Sayed Mahdi Selfia Dela Utari Siti Aminah Sudirman Sirait Suhani Suhani Syaiful Syaiful Syarifuddin Nasution Yaasin, Muhammad Yovi Pratama Yuliana Yuliana Yusra, Andi Yuswar Yunus Zainuddin Zainuddin Zulfahrizal Zulfahrizal Zulfakri