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<![CDATA[PEMBUATAN BERBAGAI PRODUK OLAHAN IKAN BAGI KELOMPOK TANI NELAYAN DI KECAMATAN SANROBONE KABUPATEN TAKALAR ]]>
<![CDATA[Ramlawati, Ramlawati]]>;
<![CDATA[Ramli, Anwar]]>
<![CDATA[ Jurnal IPA Terpadu Vol 1, No 2 (2018) ]]>
Publisher : <![CDATA[Universitas Negeri Makassar ]]>
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DOI: 10.35580/ipaterpadu.v1i2.9684
<![CDATA[Kondisi perekonomian masyarakat di Kecamatan Sanrobone masih berada pada taraf ekonomi menengah ke bawah. Permasalahan yang dihadapi oleh masyarakat di Kecamatan Sanrobone adalah kurangnya pengetahuan dan keterampilan yang dimiliki oleh masyarakat petani nelayan tentang teknologi untuk mengolah hasil perikanan menjadi produk olahan ikan siap saji yang bernilai gizi yang tinggi serta bernilai jual yang tinggi. Tujuan yang akan dicapai pada kegiatan Pengabdian Ipteks bagi Masyarakat ini adalah memberikan pengetahuan dan keterampilan kepada masyarakat kelompok tani nelayan di Kecamatan Sanrobone Kabupaten Takalar tentang teknologi pengolahan hasil perikanan menjadi produk yang siap saji yang bernilai gizi tinggi serta dapat meningkatkan pendapatan keluarga. Metode yang digunakan untuk mencapai target kegiatan pengabdian adalah memberikan penyuluhan, pelatihan, dan demonstrasi tentang teknologi pengolahan produk olahan ikan: nughet ikan, bakso ikan, otak-otak, dan empek-empek yang memiliki cita rasa tinggi dan diminati oleh masyarakat. Sasaran khusus kegiatan pengabdian Ipteks bagi Masyarakat ini adalah dua mitra kelompok masyarakat, yaitu kelompok budidaya udang windu/ikan “Harapan Kita†dan Kelompok Tani/Nelayan Usaha Pengolahan Ikan “Semoga Suksesâ€. Berdasarkan hasil evaluasi yang telah dilakukan oleh tim pengabdi, semua peserta (100%) menyatakan bahwa kegiatan ini bermanfaat untuk mengembangkan wirausaha yang dapat meningkatkan pendapatan keluarga. Masyarakat sekitar mendapat manfaat dari adanya kegiatan ini, yaitu terdapat 76% masyarakat tani nelayan menyatakan mendapat tambahan wawasan dan keterampilan dalam mengolah berbagai produk olahan ikan, yaitu penerapan sistem produksi makanan berbasis penerapan teknologi.]]>
<![CDATA[Faktor- Faktor Yang Mempengaruhi Kinerja Guru Ekonomi Pada SMA Negeri Di Kota Makassar ]]>
<![CDATA[Farmawati, Eka]]>;
<![CDATA[Ramli, Anwar]]>;
<![CDATA[Rahmatullah, Rahmatullah]]>
<![CDATA[ JEKPEND: Jurnal Ekonomi dan Pendidikan Vol 1, No 2 (2018): Juli ]]>
Publisher : <![CDATA[Universitas Negeri Makassar ]]>
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DOI: 10.26858/jekpend.v1i2.7267
<![CDATA[The purpose of this study was to determine the number of teachers, principals, work motivation, environment and teacher's welfare on economic performance in public high schools in Makassar City as a whole and simultaneously. Data collection techniques are carried out through questionnaire techniques and documentation. Data analysis techniques are Normality Test, Multiple Linear Regression Analysis, T-Test and F Test. The results showed that partially and simultaneously teacher competency, principal leadership, teacher work motivation, work environment and teacher welfare had a significant and significant effect on the performance of economic teachers in public high schools in Makassar City]]>
<![CDATA[Pendekatan Scientific Berbantuan Media Pembelajaran Puzzle dalam Meningkatkan Curiosity dan Kemampuan Analogi Mahasiswa pada Pembelajaran Ekonomi ]]>
<![CDATA[Nur, Andi St. Aisyah]]>;
<![CDATA[Ramli, Anwar]]>;
<![CDATA[Inanna, Inanna]]>
<![CDATA[ JEKPEND: Jurnal Ekonomi dan Pendidikan Vol 4, No 2 (2021): Juli ]]>
Publisher : <![CDATA[Universitas Negeri Makassar ]]>
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DOI: 10.26858/jekpend.v4i2.21919
<![CDATA[This research is based on real conditions in the field, namely the low curiosity and analogy ability of students at the University of Muhammadiyah Bone, Campus 3 Kahu, through economics learning, especially in the subject of Introduction to Economics II, Taxation on the Basics of Social Sciences which has not used a scientifically assisted approach. puzzle learning media in the learning process. This type of research uses quantitative research with the type of research design, namely Quasi Experimental Design with the type of "Nonequivalent Control Group Design". Based on the results of the recapitulation of student scores in the experimental class, the average score was 76.92 and the control class was 57.41, which means that the value of the experimental class (with a scientific approach assisted by puzzle learning media) is higher than the control class (direct approach). Meanwhile, based on the SPSS output from the Independent Samples Test obtained by the researcher, it can be concluded that the statistical test in the t-test above uses two-tailed testing which can be concluded that the value of Sig. of 0.000 < 0.05 based on the decision rule of the t-test, H1 is accepted and H0 is rejected. Thus, it can be concluded that there is an Influence of Scientific Approach on Curiosity and Analytical Ability of Students in Economic Learning at Muhammadiyah University Bone, Campus 3 Kahu, Bone Regency in economic learning]]>
<![CDATA[Pengaruh Pertumbuhan Aset dan Struktur Modal terhadap Profitabilitas Perusahaan Sub Sektor Telekomunikasi di BEI ]]>
<![CDATA[Yunika Priscilla]]>;
<![CDATA[Anwar Ramli]]>;
<![CDATA[Anwar Anwar]]>
<![CDATA[ Tirtayasa Ekonomika Vol 16, No 2 (2021) ]]>
Publisher : <![CDATA[FEB Universitas Sultan Ageng Tirtayasa ]]>
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DOI: 10.35448/jte.v16i2.12050
<![CDATA[Penelitian ini bertujuan untuk mengetahui pengaruh pertumbuhan aset dan struktur modal (DER) terhadap profitabilitas (ROE) dengan menggunakan variabel kontrol ukuran perusahaan. Populasi dalam penelitian ini yaitu seluruh perusahaan yang ada di sub sektor telekomunikasi, sedangkan sampel yang digunakan adalah 5 perusahaan sub sektor telekomunikasi dengan periode pengamatan 2008-2019, dimana pengambilan sampelnya menggunakan teknik purposive sampling. Pengumpulan data menggunakan teknik dokumentasi dan analisis data menggunakan regresi linear berganda.Penelitian ini memperoleh hasil bahwa adanya pengaruh positif dan signifikan antara variabel pertumbuhan aset terhadap profitabilitas (ROE), hal ini berarti semakin tinggi persentase pertumbuhan aset maka semakin bertambah pula kemampuan perusahaan untuk menghasilkan laba (profit) serta menunjukkan bahwa pertumbuhan aset yang meningkat akan diikuti pula dengan meningkatnya profitabilitas perusahaan dalam hal ini laba bersih yang menjadi hak pemegang saham perusahaan (ROE).Struktur modal (DER) berpengaruh negatif dan signifikan tehadap profitabilitas (ROE), arah yang negatif ini berarti semakin besar nilai DER yang dimiliki perusahaan yang diidentifikasikan dengan nilai total utang yang besar maka akan memperkecil nilai profitabilitas (ROE) yang diperoleh perusahaan. Dan secara bersama-sama (simultan) pertumbuhan aset dan DER berpengaruh signifikan terhadap profitabilitas (ROE), dimana pertumbuhan aset menggambarkan pertumbuhan aktiva perusahaan yang akan mempengaruhi profitabilitas perusahaan sedangkan struktur modal (DER) digunakan perusahaan agar mampu memaksimumkan profitabilitas dan nilai suatu perusahaan. <img 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]]>
<![CDATA[Risk dan Return Saham Perusahaan Industri Barang Konsumsi di Bursa Efek Indonesia ]]>
<![CDATA[Anwar Ramli]]>
<![CDATA[ Jurnal Aplikasi Manajemen Vol 8, No 4 (2010) ]]>
Publisher : <![CDATA[Jurusan Manajemen Fakultas Ekonomi dan Bisnis Universitas Brawijaya ]]>
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<![CDATA[This research aims to know describe risk and return stock of Consumption Goods Industry Corporation at the Indonesian Stock Exchange. This research used documentation method to collect data. Data gets from The Center for Information Capital Market (Pusat Informasi Pasar Modal) in Makassar. The sample are 15 corporations be take from 36 corporations of Consumption Goods Industry Corporation Group in Indonesian Stock Exchange (Bursa Efek Indonesia) in the year 2008. In this research used Capital Asset Pricing Model (CAPM) for data analysis. The results of research indicate that stocks of Consumption Goods Industry Corporation have β < 1, it means that stocks have characteristic of defensive. The results of analysis also indicate that expectation return from each kind of stock accompany risk. More and more increased of β, is more also of expectation return of the stock. After analyze correlation between risk rate and return of stock from 15 samples, only five of stocks are feasible to buy, there are the stocks of Mandom Indonesia Tbk (TCID), Indofood Sukses Makmur, Tbk (INDF), Bentoel International Investama, Tbk (RMBA), HM. Sampoerna, Tbk (HMSP), and Kimia Farma, Tbk (KAEF). While 10 other stocks are not feasible to buy it, or better to sell it.Keywords: Risk and Return of Stock]]>
<![CDATA[ANALYSIS CURIOSITY AND ANALOGY ABILITIES OF COLLEGE STUDENT REVIEWED FROM A SCIENTIFIC APPROACH AT THE UNIVERSITY OF MUHAMMADIYAH BONE ]]>
<![CDATA[A. St. Aisyah Nur]]>;
<![CDATA[Anwar Ramli]]>;
<![CDATA[Mrs. Inanna]]>;
<![CDATA[Andi Muhamad Iqbal Akbar Asfar]]>;
<![CDATA[A. M. Irfan Taufan Asfar]]>;
<![CDATA[Mrs. Ernawati]]>
<![CDATA[ JIRA: Jurnal Inovasi dan Riset Akademik Vol 2, No 5 (2021) ]]>
Publisher : <![CDATA[Ahlimedia Press ]]>
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DOI: 10.47387/jira.v2i5.122
<![CDATA[Curiosity is an attitude and action that always tries to find out more deeply and broadly from something that is learned. Meanwhile, analogy is a thought process to draw conclusions based on similar processes or data. This is inversely proportional to the reality in the field based on the observations of researchers at University of Muhammadiyah Bone, researchers see that the curiosity and analogy skills of college student in the economic learning process are still classified as very low. Lecturers can strive for learning by using innovative, effective and creative learning, which can provide opportunities and encourage college student to have high curiosity in order to practice their analogical reasoning skills. In connection with this, the efforts made by researchers to build curiosity and analogy skills of college student in economic learning, namely the need for a scientific approach or a scientific process-based approach which is the organization of learning experiences in a logical order including the learning process by observing, questioning, collecting information, reason (associate) and communicate. The type of research used is quantitative research using experimental quantitative methods. The research design used, namely Quasi Experimental Design with the form of Non-equivalent Control Group Design. The results of this study are proven by the results of the analysis of the recapitulation of the student's analogy ability test which can be seen in the average final test in the experimental class, which is 7,621 and the initial test reaches a value of 6,961. While the results of the final test recapitulation of the analogy ability of college student in the control class reached a value of 6,276 while the initial test results reached a value of 7.5. While the results of student responses are in the valid and reliable category.]]>
<![CDATA[Peran Perempuan Pedagang Dalam Meningkatkan Kesejahteraan Ekonomi Keluarga Di Desa Bua Kecamatan Tellulimpoe Kabupaten Sinjai ]]>
<![CDATA[Nurliana Nurliana]]>;
<![CDATA[Anwar Ramli]]>;
<![CDATA[Darman Manda]]>
<![CDATA[ Phinisi Integration Review Volume 5 Nomor 2 Tahun 2022 ]]>
Publisher : <![CDATA[Universitas Negeri Makassar ]]>
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DOI: 10.26858/pir.v5i2.33603
<![CDATA[Women generally have a dual role, namely as house wives and breadwinners. As a housewife, you are required to be able to complete family work. Meanwhile, as breadwinners, women are required to work in order to earn income to meet the daily needs of their families. This study aims to I) Find out how the role of women traders in improving the economic welfare of the family in Bua Village, Tellulimpoe District, Sinjai Regency. II) Knowing the reasons that encourage women to work as traders in improving the economic welfare of the family in Bua Village, Tellulimpoe District, Sinjai Regency. This type of research is classified as qualitative research. In this study, the informants were 6 people. This research uses observation, interview, and documentation techniques. The results of the study show that: I) Women with a profession as traders can improve the economic welfare of the family. They are not only skilled in the domestic sphere, but also in the public sphere. Women with their abilities are able to help families get out of poverty. II) There are four factors or reasons why women trade, namely the independence factor, the capital factor, the family factor and the coercive factor]]>
<![CDATA[Pengaruh Pengetahuan Kewirausahaan Dan Motivasi Secara Parsial Terhadap Perilaku Berwirausaha Siswa ]]>
<![CDATA[Andi Nurhasanah]]>;
<![CDATA[Chalid Imran Musa]]>;
<![CDATA[Anwar Ramli]]>
<![CDATA[ Phinisi Integration Review Volume 5 Nomor 1 Tahun 2022 ]]>
Publisher : <![CDATA[Universitas Negeri Makassar ]]>
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DOI: 10.26858/pir.v5i1.31497
<![CDATA[Penelitian ini bertujuan untuk mengetahui: (1) Pengaruh pengetahuan kewirausahaan dan motivasi secara parsial terhadap perilaku berwirausaha siswa di SMK Negeri 1 Makassar; (2) Pengaruh pengetahuan kewirausahaan dan motivasi secara simultan terhadap perilaku berwirausaha siswa di SMK Negeri 1 Makassar. (3) Variabel yang berpengaruh paling dominan terhadap perilaku berwirausaha siswa di SMK Negeri 1 Makassar. Jenis penelitian ini adalah penelitian kuantitatif dengan jumlah populasi 302 siswa yang terdiri atas siswa kelas X, XI dan XII pada Jurusan Bisnis Daring dan Pemasaran (BDP) di SMK Negeri 1 Makassar. Teknik pengambilan sampel menggunakan proportionate stratified random sampling dengan sampel yang berjumlah 75 siswa dari berbagai tingkatan kelas yakni kelas X, XI dan XII Jurusan Bisnis Daring dan Pemasaran (BDP). Teknik pengumpulan data menggunakan observasi, dokumentasi, serta kuesioner/angket. Teknik analisis data yang digunakan adalah uji instrumen, analisis deskriptif, uji prasyarat, dan uji hipotesis dengan menggunakan bantuan program SPSS 21 for windows Hasil penelitian menunjukkan bahwa: (1) Terdapat pengaruh yang signifikan pengetahuan kewirausahaan dan motivasi secara parsial terhadap perilaku berwirausaha siswa di SMK Negeri 1 Makassar; (2) Terdapat pengaruh yang signifikan pengetahuan kewirausahaan dan motivasi secara simultan terhadap perilaku berwirausaha siswa di SMK Negeri 1 Makassar; (3) Variabel yang paling dominan berpengaruh terhadap perilaku berwirausaha siswa di SMK Negeri 1 Makassar adalah variabel motivasi yakni sebesar 63% .]]>
<![CDATA[PKM pelatihan model manajemen edu–finance UMKM konveksi di Kota Makassar ]]>
<![CDATA[Anwar Ramli]]>;
<![CDATA[Nurhaedah Nurhaedah]]>
<![CDATA[ Seminar Nasional Pengabdian Kepada Masyarakat Vol 2019, No 9: PROSIDING 9 ]]>
Publisher : <![CDATA[Seminar Nasional Pengabdian Kepada Masyarakat ]]>
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<![CDATA[Training can provide tangible changes for UMKM Konveksi to grow, expand and develop abilities specifically inmanaging their management financial independently and even able to increase profitability. Problems to help UMKM Konveksiin this training study, analyze and implement the implementation of management financial with an management edu-financemodel for UMKM Konveksi in Makassar. The indicators used as benchmarks for evaluating / implementing the training areactivities: 1) The ability of participants to understand the material provided, 2) The ability of participants to manage data andanalyze so that it becomes useful information, and 3) Discipline, participation, enthusiasm, and motivation of participants duringthe training. The training can be utilized directly by UMKM Konveksi, including: 1) The benefits that can be felt by theparticipants for the material presented either in the form of theoretical, simulation and independently adapted practices based ontheir tasks that will support their work with the fashion management edu- finance; 2) The ability of participants in practicing howto use activities, activities in the provision of funds, and managing assets that have been applied, as well as UMKM Konveksiperform practices on how to make simple financial statements that will be practiced directly in the business so that UMKMKonveksi can maximizing the profit that can be obtained, and 3) UMKM Konveksi Participants are very disciplined,participating, enthusiastic, and motivated during the training.]]>
<![CDATA[Peran media dalam meningkatkan efektivitas belajar ]]>
<![CDATA[Anwar Ramli]]>;
<![CDATA[Rahmatullah Rahmatullah]]>;
<![CDATA[Inanna Inanna]]>;
<![CDATA[Taslim Dangnga]]>
<![CDATA[ Seminar Nasional Pengabdian Kepada Masyarakat Vol 2018, No 1: Prosiding 1 ]]>
Publisher : <![CDATA[Seminar Nasional Pengabdian Kepada Masyarakat ]]>
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<![CDATA[This paper aims to provide understanding to students of the Teacher Certification Program (PPG) on the importance of developing learning media as an effort to improve the process and quality of learning in schools. This training was carried out in the form of instructional media training that lasted for two days starting on August 4-5 2018. The material presented in this training consisted of two main themes, the basic concepts of learning media and the practical exercises in developing a learning media. This training involved 14 students of Teacher Certification Program in the Economic Education Study Program at Universitas Negeri Makassar. The material delivery method of the training consists of the lecturing, question and answer session, discussion and drill.]]>
