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All Journal Jurnal Tirtayasa Ekonomika (Tirtayasa Economica Journal) JURNAL MANAJEMEN IKRA-ITH EKONOMIKA Penentuan harga obligasi dan teori tingkat bunga PINISI Discretion Review JEKPEND Jurnal Ekonomi dan Pendidikan Community Development Journal: Jurnal Pengabdian Masyarakat Sibatik Journal : Jurnal Ilmiah Bidang Sosial, Ekonomi, Budaya, Teknologi, Dan Pendidikan Seminar Nasional Pengabdian Kepada Masyarakat NEAR: Jurnal Pengabdian kepada Masyarakat Populer: Jurnal Penelitian Mahasiswa Jurnal Manajemen dan Bisnis Kompeten: Jurnal Ilmiah Ekonomi dan Bisnis Jurnal Manajemen Benefit : Journal of Bussiness, Economics, and Finance Seminar Nasional Hasil Penelitian LP2M UNM Indo-Fintech Intellectuals: Journal of Economics and Business Economos : Jurnal Ekonomi dan Bisnis Ininnawa: Jurnal Pengabdian Masyarakat Akuntansi: Jurnal Riset Ilmu Akuntansi Jurnal Pengabdian Masyarakat Cross-Border Journal of Business Management Journal Of Sport Education, Coaching, And Health SMART: Jurnal Pengabdian Kepada Masyarakat Jurnal Bersama Ilmu Ekonomi KREATIF: Jurnal Pengabdian Masyarakat Nusantara Jurnal Manajemen, Bisnis dan Kewirausahaan Harmoni Economics: International Journal of Economics and Accounting Optimal: Jurnal Ekonomi dan Manajemen
Anwar Anwar
Universitas Negeri Makassar
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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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
PKM Edukasi Bisnis: Manajemen Usaha pada Usaha Gerabah Anwar Anwar; Inanna Inanna; Nurjannah Nurjannah
Seminar Nasional Pengabdian Kepada Masyarakat SEMINAR NASIONAL 2021 : PROSIDING EDISI 5
Publisher : Seminar Nasional Pengabdian Kepada Masyarakat

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Abstract

Abstrak. Mitra Pengabdian Kepada Masyarakat (PKM) ini adalah pemilik usaha mikro kecil gerabah. Pengetahuan tentang manajemen usaha masih sangat terbatas pada pemilik usaha gerabah. Adapun tujuan dari pengabdian ini adalah memotivasi pemilik usaha gerabah dalam memiliki pengetahuan dan keterampilan untuk bisa melakukan manajemen usaha yang benar sebagai pendukung dalam pengembangan usahanya. Metode pelaksanaan mengggunakan kombinasi ceramah dan praktek. Peserta terdiri atas pemilik usaha gerabah di Kelurahan Pallantikan Kecamatan Pattallassang Kabupaten Takalar. Teknik pelaksanaan kegiatan terdiri atas tiga tahap dimulai dari tahap persiapan, tahap pelaksanaan, dan tahap evaluasi kegiatan. Hasil yang dicapai dari pengabdian ini adalah (1) pemilik usaha sudah memahami analisis kelayakan usaha dengan titik pulang pokok dan potensi pasar, (2) pemilik usaha sudah memiliki pengetahuan dalam menentukan optimalisasi laba, (3) pemilik usaha sudah mampu menganalisis kinerja keuangan. Keterbatasan dalam pelatihan ini adalah metode pelaksanaan belum menggunakan teknik pendampingan, sehingga pengetahuan manajemen strategi belum dapat diimplementasikan dalam aktivitas usaha.Kata Kunci: Manajemen Usaha, Usaha Gerabah
Pelatihan Manajemen Keuangan Pada Pemilik Usaha Gerabah di Kecamatan Pattallassang Kabupaten Takalar Anwar Anwar; Sitti Hasbiah; Abd. Muis Dilla; Hety Budiyanti
Seminar Nasional Pengabdian Kepada Masyarakat PROSIDING EDISI 10: SEMNAS 2020
Publisher : Seminar Nasional Pengabdian Kepada Masyarakat

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Abstract

Abstrak. Pengetahuan tentang manajemen keuangan merupakan salah satu bagian utama dalam membangun kelanjutan dan daya saing usaha. Membekali para pemilik dengan pengetahuan tersebut akan sangat membantu perkembangan profesionalisme mereka dalam manajemen usaha. Adapun tujuan dari pengabdian ini adalah memotivasi pemilik usaha gerabah dalam memiliki pengetahuan dan keterampilan untuk bisa melakukan manajemen keuangan yang benar sebagai penunjang dalam pengembangan usahanya. Pengabdian ini mengggunakan kombinasi metode ceramah dan praktek. Peserta terdiri atas pemilik usaha gerabah di Kecamatan Pattallassang Kabupaten Takalar. Teknik pelaksanaan kegiatan terdiri atas tiga tahap dimulai dari tahap persiapan, tahap pelaksanaan, dan tahap evaluasi kegiatan. Hasil pengabdian menunjukkan bahwa pemilik usaha gerabah kesulitan dalam melakukan manajemen keuangan karena masih terbiasa dengan manajemen konvensional. Hasil yang dicapai dari pengabdian ini adalah (1) Pemilik usaha gerabah sudah mampu melakukan analisis pendanaan, (2) Mampu mengenal teknik-teknik dalam analisis investasi, (3) Sudah mengetahui  analisis pengelolaan aset. Keterbatasan dalam pelatihan ini adalah metode pelaksanaan belum menggunakan teknik pendampingan, sehingga pengetahuan tentang penyusunan setiap jenis laporan keuangan dapat diimplementasikan dalam aktivitas usaha.  Kata Kunci: Manajemen Keuangan, Usaha Gerabah
Penggunaan single index model dalam pembentukan portofolio optimal saham syariah Wilda Wilda; Uhud Darmawan Natsir; Anwar Anwar
JURNAL MANAJEMEN Vol 14, No 3 (2022): September
Publisher : Faculty of Economics and Business Mulawarman University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/jmmn.v14i3.11551

Abstract

Penelitian ini bertujuan untuk mengetahui saham-saham perusahaan pada Jakarta Islamic Index (JII) yang membentuk portofolio optimal menggunakan single index model periode Juni 2017- November 2021, mengetahui proporsi dana masing-masing saham perusahaan yang membentuk portofolio optimal, serta mengetahui  return dan risiko dari portofolio optimal. Jenis penelitian ini adalah penelitian deskriptif dengan pendekatan kuantitatif. Populasi dalam penelitian ini adalah seluruh saham perusahaan yang tergabung dalam Jakarta Islamic Index periode Juni 2017 - November 2021. Adapun yang menjadi sampel dalam penelitian adalah 46 saham perusahaan yang termasuk dalam kriteria yang ditetapkan peneliti. Teknik pengumpulan data dilakukan dengan dokumentasi. Teknik analisis data menggunakan single index model. Hasil penelitian menunjukkan  bahwa 16 saham perusahaan sebagai penyusun portofolio optimal  yaitu saham ACES sebesar 1,52% ADRO sebesar 0,44%, ANTM sebesar 5,50%, BRPT sebesar 6,80%, CPIN sebesar 10,80%, ERAA sebesar 9,45%, INCO sebesar 7,42% INDY sebesar 3,83%, INKP sebesar 7,11%, ITMG sebesar 2,65% JPFA sebesar 2,10% MDKA sebesar 26,48%, MIKA sebesar 2,16%, PTBA sebesar 1,62% TKIM sebesar 8,80%, dan TPIA sebesar 3,30%. Expected return yang didapatkan investor dari portofolio optimal yang terbentuk adalah sebesar 0,0351 atau 3,51%. Risiko portofolio yang ditanggung oleh investor atas investasi dari portofolio saham optimal tersebut adalah 0,0066 atau 0,66%.
Financial Literature to Sustainbility Micro Business in Gowa Regency Serly Serly; Anwar Anwar; Nurman Nurman
PINISI Discretion Review Volume 6, Issue 1, September 2022
Publisher : Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26858/pdr.v6i1.37047

Abstract

This research is a qualitative research that aims to find out how the level of financial literacy on the sustainability of Micro Enterprises in Gowa Regency. The sample in this study consisted of 5 informants of micro business actors. Techniques for analyzing research data using the help of ATLAS.ti 9. The results of the analysis show that micro-enterprises are classified as not literate, so the financial literacy of micro-enterprises is still lacking. It can be concluded that micro-enterprises have no potential for business sustainability. 
Pengaruh pengalaman kerja terhadap kinerja pegawai Irawati Irawati; Anwar Anwar; Zainal Ruma; Muh. Ikhwan Maulana Haeruddin; Tenri S.P Dipoatmodjo
JURNAL MANAJEMEN Vol 14, No 4 (2022): Desember
Publisher : Faculty of Economics and Business Mulawarman University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/jmmn.v14i4.11560

Abstract

Penelitian ini bertujuan untuk mengetahui pengaruh Pengalaman Kerja berpengaruh signifikan terhadap Kinerja Pegawai pada Kantor Satpol PP Kabupaten Barru. Penelitian ini merupakan penelitin kuantitatif, merupakan penelitian populasi. Subjek penelitian ini adalah karywan di Kantor Pegawai Satpol PP Kabupaten Barru dengan jumlah 37 orang. Pengumpulan data menggunakan kuisioner yang telah di uji validitas dan reliabilitasnya dengan analisis data dilakukan dengan menggunaan analisis regresi linear berganda yang menggunakan dua metode yaitu uji signifikan dengan menggunakan bantuan olah data Statistical Product and Service Solution (SPSS). Hasil penelitian ini menunjukkan bahwa Pengalaman kerja mempunyai pengaruh pengalaman kerja terhadap kinerja pegawai pada kantor Satpol PP kabupaten Barru, hal ini menunjukkan bahwa semakin baik pengalaman kerja maka semakin baik pula kinerja pegawai pada kanttor Satpol PP Kabupaten Barru, variabel pengalaman kerja terhadap kinerja pegawai pada variabel pengalaman kerja yang menunjukan nilai tertinggi terdapat pada indikator tingkat pengatahuan dan keterampilan, hal tersebut dilihat dari sebagian besar pegawai yang mampu untuk saling menghargai sesama pegawai. Sedangkan pada variabel kinerja pegawai yang menunjukan nilai tertinggi terdapat pada indikator kuantitas dan kualitas.
PENGARUH KETERAMPILAN, PENGETAHUAN DAN KEMAMPUAN SDM TERHADAP KINERJA KARYAWAN AGROINDUSTRI DANGKE DI KELURAHAN MATARAN KECAMATAN ANGGERAJA Anwar Anwar; Aulia Nurul Izmi; Agung Widhi Kurniawan
SIBATIK JOURNAL: Jurnal Ilmiah Bidang Sosial, Ekonomi, Budaya, Teknologi, dan Pendidikan Vol. 2 No. 2 (2023): January
Publisher : Lafadz Jaya Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54443/sibatik.v2i2.534

Abstract

This objective of this research to determine the influence of skills, knowledge and human resources capabilities on the performance of dangke agro-industrial employees in the materi Village. In addition, this application can also be used to improve computer performance. The sample in this study using saturated sample techniques with the number of 30 respondents.This study was conducted using multiple lienar regression analysis. The results showed that there was a positive and significant influence on the overall ability, knowledge and ability of human resources on employee performance with a presentation of 91.1%. This shows the meaning that the dependent variable (employee performance) can be interpreted by the ability, knowledge and ability of human resources by 91.1%, while 8.9% (100% - 91.1%) is expressed by the cause and other factors that are under the variable ability, knowledge and ability of human resources studied. Such as aspects of salary or salary allowances (bonuses) required, work motivation to discipline individual employees in carrying out work in the agro-industry dangke Mataran Village.
Analisis Portofolio Optimal Dengan Model Markowitz pada Perusahaan Sektor Basic Materiasl di BEI Syarif Hidayatullah; Zainal Ruma; Anwar Anwar
Jurnal SEKURITAS (Saham, Ekonomi, Keuangan dan Investasi) Vol 6, No 2 (2023): Jurnal SEKURITAS
Publisher : Prodi Manajemen Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Penelitian ini bertujuan untuk mengatahui pembentukan portofolio optimal dengan menggunakan model Markowitz pada saham sektor basic materials di Bursa Efek Indonesia. Populasi dalam penelitian ini adalah seluruh saham sektor basic materials berdasarkan pembagian IDX IC di Bursa Efek Indonesia periode April 2021 – Maret 2022 yaitu sebanyak 93 saham, sedangkan yang menjadi sampel adalah 71 saham. Analisi data dilakukan dengan tahapan model Markowitz yang dimulai dengan mengupulkan data harga saham per hari selama periode penelitian, menghitung return realisasi, expected return, varian, standar deviasi, kovarian, korelasi, sampai mendapatkan portofolio optimal dengan risiko dan expected returnnya. Hasil penelitian menunjukkan bahwa, pembentukan portofolio optimal dengan model Markowitz pada perusahaan sektor basic materials periode April 2021-Maret 2022 didapatkan ada 35 saham yang masuk dalam portofolio optimal dengan expected return 0,118% dan risiko 0,56%
Analisis Portofolio Optimal Dengan Model Markowitz pada Perusahaan Sektor Basic Materiasl di BEI Syarif Hidayatullah; Zainal Ruma; Anwar Anwar
Jurnal SEKURITAS (Saham, Ekonomi, Keuangan dan Investasi) Vol 6, No 2 (2023): Jurnal SEKURITAS
Publisher : Prodi Manajemen Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/skt.v6i2.24085

Abstract

Penelitian ini bertujuan untuk mengatahui pembentukan portofolio optimal dengan menggunakan model Markowitz pada saham sektor basic materials di Bursa Efek Indonesia. Populasi dalam penelitian ini adalah seluruh saham sektor basic materials berdasarkan pembagian IDX IC di Bursa Efek Indonesia periode April 2021 – Maret 2022 yaitu sebanyak 93 saham, sedangkan yang menjadi sampel adalah 71 saham. Analisi data dilakukan dengan tahapan model Markowitz yang dimulai dengan mengupulkan data harga saham per hari selama periode penelitian, menghitung return realisasi, expected return, varian, standar deviasi, kovarian, korelasi, sampai mendapatkan portofolio optimal dengan risiko dan expected returnnya. Hasil penelitian menunjukkan bahwa, pembentukan portofolio optimal dengan model Markowitz pada perusahaan sektor basic materials periode April 2021-Maret 2022 didapatkan ada 35 saham yang masuk dalam portofolio optimal dengan expected return 0,118% dan risiko 0,56%
Model Markowitz Dalam Keputusan Investasi Saham Pada Index Lq45 di Bursa Efek Indonesia Siti Hasbiah; Anwar Anwar; Basri Bado
JEKPEND: Jurnal Ekonomi dan Pendidikan Vol 5, No 1 (2022): Januari
Publisher : Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26858/jekpend.v5i1.24709

Abstract

Penelitian ini bertujuan untuk mengetahui model Markowitz pada saham Indeks LQ45, sehingga diperoleh komposisi saham yang dapat dijadikan pilihan investasi atau membentuk portofolio. Populasi penelitian ini adalah seluruh saham-saham emiten atau perusahaan go public yang pernah masuk dalam Indeks LQ45 di Bursa Efek Indonesia periode Februari 2017-Januari 2021 sebanyak 66 saham perusahaan dan sampel penelitian ini adalah 65 saham perusahaan yang dipilih berdasarkan teknik nonprobability sampling dengan metode purposive sampling. Pengumpulan data penelitian menggunakan teknik dokumentasi. Analisis data dilakukan model Markowitz dengan mengumpulkan data harga saham penutupan (closing price) bulanan sampai mendapatkan portofolio optimal kemudian mengukur kinerja portofolio optimal dari model Markowitz. Berdasarkan hasil penelitian terdapat 11 saham pembentuk portofolio optimal model Markowitz dengan expected return portofolio sebesar 0,96% per bulan dengan risiko portofolio (varianportofolio sebesar 0,12% per bulan dan standar deviasi sebesar 3,41% per bulan).
Co-Authors A. Nur Habibie Jannah Abadi, Rahmat Riwayat Abd. Muis Dilla Abd. Rahman Abdul Rahman Abdul Rahman Aditya Fajri Mustakim Agung Widhi Kurniawan Ahmad Raihan Al Bustani Ahmad Rum Aisyah Abidin Aldi Alwi Alvian Hijrah Amiruddin Tawe Amiruddin Tawe Andi Bintang Maulana A Andi Fadel Muhammad Andi Mustika Andi Mustika Amin Annisa Awaliyah Ridwan Anwar Ramli Arhan Tri Putra Aslam, Annisa Paramaswary Aulia Nurul Izmi Basri Bado Burhanuddin Burhanuddin Chalid Imran Musa Chalid Imran Musa Deddy Ibrahim Rauf Dwi Anugerah Lestari Musa Fitriani Rahim Gufrana Gufrana Hardianti Hardianti Hety Budianty Hety Budiyanti Ilma Wulansari Hasdiansa IlmaWulansari Hasdiansa Inanna Inaz Zahrah Idris Irawati Irawati Khaidir Syahrul Khutbah Nur Rusman DH M. Ikhwan Maulana Haeruddin Marsena Limbong Moh. Alief Rezkiyah Putra Alwiwikh Muhammad Idris Muhammad Rijal Alim Rahmat Muhammad Syafri Muis Dilla Muliana Agus Munadhir Munadhir Musfirah Mustika Amin Nur Alya Nanda Nur Athifah Wahyuningsih Nur Fadilah Ayu Sandira Nur Haliza Nurbayani Nurbayani Nurjannah Nurlina Nurlina Nurman Nurman Putri Aliyah Natasya Rahim, Abd Rezki Amaliah Alim Rezky Amalia Hamka Ridfan Rifadly Abadi Riska Fauzia Riska Yuniarti Romansyah Sahabuddin Sahabuddin Sahabuddin Sastry Armelia Tokilos Serly Serly Siti Alifah Tzahdiah Siti Hasbiah Sitti Hasbiah Stefanie Inggried Gorap sulistiawaty sulis Sun Dewi Putri Syarif Hidayatullah Tenri S P Dipoatmodjo Tenri Sayu Puspitaningsih Dipoatmodjo Trisno Setiawan Usman Usman Wahyu Veriadi Ramadhan Wilda Wilda Windarsari, Wiwin Riski Windi Atriani Yunika Priscilla Yusri Karmila Zainal Ruma