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PEMBUATAN BERBAGAI PRODUK OLAHAN IKAN BAGI KELOMPOK TANI NELAYAN DI KECAMATAN SANROBONE KABUPATEN TAKALAR Ramlawati, Ramlawati; Ramli, Anwar
Jurnal IPA Terpadu Vol 1, No 2 (2018)
Publisher : Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (440.374 KB) | DOI: 10.35580/ipaterpadu.v1i2.9684

Abstract

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 Sukses”. 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.
Faktor- Faktor Yang Mempengaruhi Kinerja Guru Ekonomi Pada SMA Negeri Di Kota Makassar Farmawati, Eka; Ramli, Anwar; Rahmatullah, Rahmatullah
JEKPEND: Jurnal Ekonomi dan Pendidikan Vol 1, No 2 (2018): Juli
Publisher : Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (257.611 KB) | DOI: 10.26858/jekpend.v1i2.7267

Abstract

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
Pendekatan Scientific Berbantuan Media Pembelajaran Puzzle dalam Meningkatkan Curiosity dan Kemampuan Analogi Mahasiswa pada Pembelajaran Ekonomi Nur, Andi St. Aisyah; Ramli, Anwar; Inanna, Inanna
JEKPEND: Jurnal Ekonomi dan Pendidikan Vol 4, No 2 (2021): Juli
Publisher : Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26858/jekpend.v4i2.21919

Abstract

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
Analisis Pengembalian Bantuan Dana Bergulir Melalui Program Nasional Pemberdayaan Masyarakat (PNPM) Mandiri Perkotaan di Kota Makassar (Studi Kasus BKM ”Maccini Salewangang” Kelurahan Maccini Kecamatan Makassar) Anwar Ramli
Jurnal Aplikasi Manajemen Vol 11, No 2 (2013)
Publisher : Jurusan Manajemen Fakultas Ekonomi dan Bisnis Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (65.117 KB)

Abstract

Abstract: The problem statements of this research were whether the factors of LAR, PAR, CCr, and ROI gave significant influence on aid fund repayment at BKM Maccini Salewangang PNPK Makassar, and which factor that influences the aid fund repayment dominantly. This research aims to analyze the role of LAR, PAR, CCr and ROI variables on aid fund repayment at BKM Maccini Salewangang, Makassar City as wellas to analyze the dominant variable which influences aid fund repayment at BKM Maccini Salewangang, Makassar City. The result reveals that the partial influence among LAR, PAR, CCr, and ROI variables on the aid fund repayment particularly at BKM Maccini Salewangang demonstrating that LAR and PAR variables give negative influence on the aid fund repayment as high LAR and PAR causes the aid fund repaymentbecome low. The result of the regression test indicates that the dominant variable which influences the aid fund repayment is ROI as the higher the ROI, results in more current aid fund repayment particularly at BKM Maccini Salewangang. Keywords: fund repayment, national community empowerment program
Pengaruh Pertumbuhan Aset dan Struktur Modal terhadap Profitabilitas Perusahaan Sub Sektor Telekomunikasi di BEI Yunika Priscilla; Anwar Ramli; Anwar Anwar
Tirtayasa Ekonomika Vol 16, No 2 (2021)
Publisher : FEB Universitas Sultan Ageng Tirtayasa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35448/jte.v16i2.12050

Abstract

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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
Risk dan Return Saham Perusahaan Industri Barang Konsumsi di Bursa Efek Indonesia Anwar Ramli
Jurnal Aplikasi Manajemen Vol 8, No 4 (2010)
Publisher : Jurusan Manajemen Fakultas Ekonomi dan Bisnis Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1352.98 KB)

Abstract

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
ANALYSIS CURIOSITY AND ANALOGY ABILITIES OF COLLEGE STUDENT REVIEWED FROM A SCIENTIFIC APPROACH AT THE UNIVERSITY OF MUHAMMADIYAH BONE A. St. Aisyah Nur; Anwar Ramli; Mrs. Inanna; Andi Muhamad Iqbal Akbar Asfar; A. M. Irfan Taufan Asfar; Mrs. Ernawati
JIRA: Jurnal Inovasi dan Riset Akademik Vol 2, No 5 (2021)
Publisher : Ahlimedia Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47387/jira.v2i5.122

Abstract

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.
Peran Perempuan Pedagang Dalam Meningkatkan Kesejahteraan Ekonomi Keluarga Di Desa Bua Kecamatan Tellulimpoe Kabupaten Sinjai Nurliana Nurliana; Anwar Ramli; Darman Manda
Phinisi Integration Review Volume 5 Nomor 2 Tahun 2022
Publisher : Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26858/pir.v5i2.33603

Abstract

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
Pengaruh Pengetahuan Kewirausahaan Dan Motivasi Secara Parsial Terhadap Perilaku Berwirausaha Siswa Andi Nurhasanah; Chalid Imran Musa; Anwar Ramli
Phinisi Integration Review Volume 5 Nomor 1 Tahun 2022
Publisher : Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26858/pir.v5i1.31497

Abstract

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% .
ANALISIS KINERJA KEUANGAN PT. JASA RAHARJA Muh Hidayatullah; Anwar Ramli; Tenri S. P. Dipoatmodjo
TRANSEKONOMIKA: AKUNTANSI, BISNIS DAN KEUANGAN Vol. 2 No. 5 (2022): September 2022
Publisher : Transpublika Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55047/transekonomika.v2i5.206

Abstract

This study aims to determine how the financial performance of PT. Jasa Raharja in 2017 to 2021. The population in this study is the company's annual financial statements, while the sample used is the company's financial statements from 2017 to 2021. The data collection technique in this study is by means of documentation, then it will be analyzed using ratios. liquidity which consists of Current Ratio, Cash Ratio and solvency ratio which consists of Debt to Assets Ratio (DAR) and Debt to Equity Ratio (DER) and profitability ratio consists of Return on Assets (ROA) and Return on Equity (ROE) which then measures financial health using the Risk Based Capital method. The results of this study indicate that the liquidity ratio where the Cash Ratio is still below the standard ratio, which means the company is less able to pay its short-term obligations using existing cash, but the company is still able to pay its short-term debt using its current assets and the solvency ratio is above the standard ratio in general, which means the company's finances are quite good in meeting long-term debt and for profitability ratios where the Return on Assets is still below the standard ratio, which means the company is less efficient in managing its assets to generate profits but in terms of Return on Equity the company has been able to manage capital owned to earn a profit, while Risk Based Capital shows the company's financial performance from 2017 to 2021 is quite good.